utils/process_bindingmoad.py

from pathlib import Path
from time import time
import random
from collections import defaultdict
import argparse
import warnings
import pickle
import os

from tqdm import tqdm
import numpy as np
import torch
from Bio.PDB import PDBParser
from Bio.PDB.Polypeptide import three_to_one, is_aa
from Bio.PDB import PDBIO, Select
from openbabel import openbabel
from rdkit import Chem
from rdkit.Chem import QED
from scipy.ndimage import gaussian_filter

from geometry_utils import get_bb_transform
from analysis.molecule_builder import build_molecule
from analysis.metrics import rdmol_to_smiles
import constants
from constants import covalent_radii, dataset_params
import moad_utils

dataset_info = dataset_params['bindingmoad']
amino_acid_dict = dataset_info['aa_encoder']
atom_dict = dataset_info['atom_encoder']
atom_decoder = dataset_info['atom_decoder']


class Model0(Select):
    def accept_model(self, model):
        return model.id == 0


def read_label_file(csv_path):
    """
    Read BindingMOAD's label file
    Args:
        csv_path: path to 'every.csv'
    Returns:
        Nested dictionary with all ligands. First level: EC number,
            Second level: PDB ID, Third level: list of ligands. Each ligand is
            represented as a tuple (ligand name, validity, SMILES string)
    """
    ligand_dict = {}

    with open(csv_path, 'r') as f:
        for line in f.readlines():
            row = line.split(',')

            # new protein class
            if len(row[0]) > 0:
                curr_class = row[0]
                ligand_dict[curr_class] = {}
                continue

            # new protein
            if len(row[2]) > 0:
                curr_prot = row[2]
                ligand_dict[curr_class][curr_prot] = []
                continue

            # new small molecule
            if len(row[3]) > 0:
                ligand_dict[curr_class][curr_prot].append(
                    # (ligand name, validity, SMILES string)
                    [row[3], row[4], row[9]]
                )

    return ligand_dict


def compute_druglikeness(ligand_dict):
    """
    Computes RDKit's QED value and adds it to the dictionary
    Args:
        ligand_dict: nested ligand dictionary
    Returns:
        the same ligand dictionary with additional QED values
    """
    print("Computing QED values...")
    for p, m in tqdm([(p, m) for c in ligand_dict for p in ligand_dict[c]
                      for m in ligand_dict[c][p]]):
        mol = Chem.MolFromSmiles(m[2])
        if mol is None:
            mol_id = f'{p}_{m}'
            warnings.warn(f"Could not construct molecule {mol_id} from SMILES "
                          f"string '{m[2]}'")
            continue
        m.append(QED.qed(mol))
    return ligand_dict


def filter_and_flatten(ligand_dict, qed_thresh, max_occurences, seed):

    filtered_examples = []
    all_examples = [(c, p, m) for c in ligand_dict for p in ligand_dict[c]
                    for m in ligand_dict[c][p]]

    # shuffle to select random examples of ligands that occur more than
    # max_occurences times
    random.seed(seed)
    random.shuffle(all_examples)

    ligand_name_counter = defaultdict(int)
    print("Filtering examples...")
    for c, p, m in tqdm(all_examples):

        ligand_name, ligand_chain, ligand_resi = m[0].split(':')
        if m[1] == 'valid' and len(m) > 3 and m[3] > qed_thresh:
            if ligand_name_counter[ligand_name] < max_occurences:
                filtered_examples.append(
                    (c, p, m)
                )
                ligand_name_counter[ligand_name] += 1

    return filtered_examples


def split_by_ec_number(data_list, n_val, n_test, ec_level=1):
    """
    Split dataset into training, validation and test sets based on EC numbers
    https://en.wikipedia.org/wiki/Enzyme_Commission_number
    Args:
        data_list: list of ligands
        n_val: number of validation examples
        n_test: number of test examples
        ec_level: level in the EC numbering hierarchy at which the split is
            made, i.e. items with matching EC numbers at this level are put in
            the same set
    Returns:
        dictionary with keys 'train', 'val', and 'test'
    """

    examples_per_class = defaultdict(int)
    for c, p, m in data_list:
        c_sub = '.'.join(c.split('.')[:ec_level])
        examples_per_class[c_sub] += 1

    assert sum(examples_per_class.values()) == len(data_list)

    # split ec numbers
    val_classes = set()
    for c, num in sorted(examples_per_class.items(), key=lambda x: x[1],
                         reverse=True):
        if sum([examples_per_class[x] for x in val_classes]) + num <= n_val:
            val_classes.add(c)

    test_classes = set()
    for c, num in sorted(examples_per_class.items(), key=lambda x: x[1],
                         reverse=True):
        # skip classes already used in the validation set
        if c in val_classes:
            continue
        if sum([examples_per_class[x] for x in test_classes]) + num <= n_test:
            test_classes.add(c)

    # remaining classes belong to test set
    train_classes = {x for x in examples_per_class if
                     x not in val_classes and x not in test_classes}

    # create separate lists of examples
    data_split = {}
    data_split['train'] = [x for x in data_list if '.'.join(
        x[0].split('.')[:ec_level]) in train_classes]
    data_split['val'] = [x for x in data_list if '.'.join(
        x[0].split('.')[:ec_level]) in val_classes]
    data_split['test'] = [x for x in data_list if '.'.join(
        x[0].split('.')[:ec_level]) in test_classes]

    assert len(data_split['train']) + len(data_split['val']) + \
           len(data_split['test']) == len(data_list)

    return data_split


def ligand_list_to_dict(ligand_list):
    out_dict = defaultdict(list)
    for _, p, m in ligand_list:
        out_dict[p].append(m)
    return out_dict


def process_ligand_and_pocket(pdb_struct, ligand_name, ligand_chain,
                              ligand_resi, dist_cutoff, ca_only,
                              compute_quaternion=False):
    try:
        residues = {obj.id[1]: obj for obj in
                    pdb_struct[0][ligand_chain].get_residues()}
    except KeyError as e:
        raise KeyError(f'Chain {e} not found ({pdbfile}, '
                       f'{ligand_name}:{ligand_chain}:{ligand_resi})')
    ligand = residues[ligand_resi]
    assert ligand.get_resname() == ligand_name, \
        f"{ligand.get_resname()} != {ligand_name}"

    # remove H atoms if not in atom_dict, other atom types that aren't allowed
    # should stay so that the entire ligand can be removed from the dataset
    lig_atoms = [a for a in ligand.get_atoms()
                 if (a.element.capitalize() in atom_dict or a.element != 'H')]
    lig_coords = np.array([a.get_coord() for a in lig_atoms])

    try:
        lig_one_hot = np.stack([
            np.eye(1, len(atom_dict), atom_dict[a.element.capitalize()]).squeeze()
            for a in lig_atoms
        ])
    except KeyError as e:
        raise KeyError(
            f'Ligand atom {e} not in atom dict ({pdbfile}, '
            f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    # Find interacting pocket residues based on distance cutoff
    pocket_residues = []
    for residue in pdb_struct[0].get_residues():
        res_coords = np.array([a.get_coord() for a in residue.get_atoms()])
        if is_aa(residue.get_resname(), standard=True) and \
                (((res_coords[:, None, :] - lig_coords[None, :, :]) ** 2).sum(-1) ** 0.5).min() < dist_cutoff:
            pocket_residues.append(residue)

    # Compute transform of the canonical reference frame
    n_xyz = np.array([res['N'].get_coord() for res in pocket_residues])
    ca_xyz = np.array([res['CA'].get_coord() for res in pocket_residues])
    c_xyz = np.array([res['C'].get_coord() for res in pocket_residues])

    if compute_quaternion:
        quaternion, c_alpha = get_bb_transform(n_xyz, ca_xyz, c_xyz)
        if np.any(np.isnan(quaternion)):
            raise ValueError(
                f'Invalid value in quaternion ({pdbfile}, '
                f'{ligand_name}:{ligand_chain}:{ligand_resi})')
    else:
        c_alpha = ca_xyz

    if ca_only:
        pocket_coords = c_alpha
        try:
            pocket_one_hot = np.stack([
                np.eye(1, len(amino_acid_dict),
                       amino_acid_dict[three_to_one(res.get_resname())]).squeeze()
                for res in pocket_residues])
        except KeyError as e:
            raise KeyError(
                f'{e} not in amino acid dict ({pdbfile}, '
                f'{ligand_name}:{ligand_chain}:{ligand_resi})')
    else:
        pocket_atoms = [a for res in pocket_residues for a in res.get_atoms()
                        if (a.element.capitalize() in atom_dict or a.element != 'H')]
        pocket_coords = np.array([a.get_coord() for a in pocket_atoms])
        try:
            pocket_one_hot = np.stack([
                np.eye(1, len(atom_dict), atom_dict[a.element.capitalize()]).squeeze()
                for a in pocket_atoms
            ])
        except KeyError as e:
            raise KeyError(
                f'Pocket atom {e} not in atom dict ({pdbfile}, '
                f'{ligand_name}:{ligand_chain}:{ligand_resi})')

    pocket_ids = [f'{res.parent.id}:{res.id[1]}' for res in pocket_residues]

    ligand_data = {
        'lig_coords': lig_coords,
        'lig_one_hot': lig_one_hot,
    }
    pocket_data = {
        'pocket_coords': pocket_coords,
        'pocket_one_hot': pocket_one_hot,
        'pocket_ids': pocket_ids,
    }
    if compute_quaternion:
        pocket_data['pocket_quaternion'] = quaternion
    return ligand_data, pocket_data


def compute_smiles(positions, one_hot, mask):
    print("Computing SMILES ...")

    atom_types = np.argmax(one_hot, axis=-1)

    sections = np.where(np.diff(mask))[0] + 1
    positions = [torch.from_numpy(x) for x in np.split(positions, sections)]
    atom_types = [torch.from_numpy(x) for x in np.split(atom_types, sections)]

    mols_smiles = []

    pbar = tqdm(enumerate(zip(positions, atom_types)),
                total=len(np.unique(mask)))
    for i, (pos, atom_type) in pbar:
        mol = build_molecule(pos, atom_type, dataset_info)

        # BasicMolecularMetrics() computes SMILES after sanitization
        try:
            Chem.SanitizeMol(mol)
        except ValueError:
            continue

        mol = rdmol_to_smiles(mol)
        if mol is not None:
            mols_smiles.append(mol)
        pbar.set_description(f'{len(mols_smiles)}/{i + 1} successful')

    return mols_smiles


def get_n_nodes(lig_mask, pocket_mask, smooth_sigma=None):
    # Joint distribution of ligand's and pocket's number of nodes
    idx_lig, n_nodes_lig = np.unique(lig_mask, return_counts=True)
    idx_pocket, n_nodes_pocket = np.unique(pocket_mask, return_counts=True)
    assert np.all(idx_lig == idx_pocket)

    joint_histogram = np.zeros((np.max(n_nodes_lig) + 1,
                                np.max(n_nodes_pocket) + 1))

    for nlig, npocket in zip(n_nodes_lig, n_nodes_pocket):
        joint_histogram[nlig, npocket] += 1

    print(f'Original histogram: {np.count_nonzero(joint_histogram)}/'
          f'{joint_histogram.shape[0] * joint_histogram.shape[1]} bins filled')

    # Smooth the histogram
    if smooth_sigma is not None:
        filtered_histogram = gaussian_filter(
            joint_histogram, sigma=smooth_sigma, order=0, mode='constant',
            cval=0.0, truncate=4.0)

        print(f'Smoothed histogram: {np.count_nonzero(filtered_histogram)}/'
              f'{filtered_histogram.shape[0] * filtered_histogram.shape[1]} bins filled')

        joint_histogram = filtered_histogram

    return joint_histogram


def get_bond_length_arrays(atom_mapping):
    bond_arrays = []
    for i in range(3):
        bond_dict = getattr(constants, f'bonds{i + 1}')
        bond_array = np.zeros((len(atom_mapping), len(atom_mapping)))
        for a1 in atom_mapping.keys():
            for a2 in atom_mapping.keys():
                if a1 in bond_dict and a2 in bond_dict[a1]:
                    bond_len = bond_dict[a1][a2]
                else:
                    bond_len = 0
                bond_array[atom_mapping[a1], atom_mapping[a2]] = bond_len

        assert np.all(bond_array == bond_array.T)
        bond_arrays.append(bond_array)

    return bond_arrays


def get_lennard_jones_rm(atom_mapping):
    # Bond radii for the Lennard-Jones potential
    LJ_rm = np.zeros((len(atom_mapping), len(atom_mapping)))

    for a1 in atom_mapping.keys():
        for a2 in atom_mapping.keys():
            all_bond_lengths = []
            for btype in ['bonds1', 'bonds2', 'bonds3']:
                bond_dict = getattr(constants, btype)
                if a1 in bond_dict and a2 in bond_dict[a1]:
                    all_bond_lengths.append(bond_dict[a1][a2])

            if len(all_bond_lengths) > 0:
                # take the shortest possible bond length because slightly larger
                # values aren't penalized as much
                bond_len = min(all_bond_lengths)
            else:
                # Replace missing values with sum of average covalent radii
                bond_len = covalent_radii[a1] + covalent_radii[a2]

            LJ_rm[atom_mapping[a1], atom_mapping[a2]] = bond_len

    assert np.all(LJ_rm == LJ_rm.T)
    return LJ_rm


def get_type_histograms(lig_one_hot, pocket_one_hot, atom_encoder, aa_encoder):

    atom_decoder = list(atom_encoder.keys())
    atom_counts = {k: 0 for k in atom_encoder.keys()}
    for a in [atom_decoder[x] for x in lig_one_hot.argmax(1)]:
        atom_counts[a] += 1

    aa_decoder = list(aa_encoder.keys())
    aa_counts = {k: 0 for k in aa_encoder.keys()}
    for r in [aa_decoder[x] for x in pocket_one_hot.argmax(1)]:
        aa_counts[r] += 1

    return atom_counts, aa_counts


def saveall(filename, pdb_and_mol_ids, lig_coords, lig_one_hot, lig_mask,
            pocket_coords, pocket_quaternion, pocket_one_hot, pocket_mask):

    np.savez(filename,
        names=pdb_and_mol_ids,
        lig_coords=lig_coords,
        lig_one_hot=lig_one_hot,
        lig_mask=lig_mask,
        pocket_coords=pocket_coords,
        pocket_quaternion=pocket_quaternion,
        pocket_one_hot=pocket_one_hot,
        pocket_mask=pocket_mask
    )
    return True


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('basedir', type=Path)
    parser.add_argument('--outdir', type=Path, default='all_data/')
    parser.add_argument('--qed_thresh', type=float, default=0.3)
    parser.add_argument('--max_occurences', type=int, default=50)
    parser.add_argument('--num_val', type=int, default=300)
    parser.add_argument('--num_test', type=int, default=300)
    parser.add_argument('--dist_cutoff', type=float, default=8.0)
    parser.add_argument('--ca_only', action='store_true')
    parser.add_argument('--random_seed', type=int, default=42)
    parser.add_argument('--make_split', action='store_true')
    args = parser.parse_args()

    pdbdir = args.basedir / 'BindingMOAD_2020/'

    # Make output directory
    if args.outdir is None:
        suffix = '' if 'H' in atom_dict else '_noH'
        suffix += '_ca_only' if args.ca_only else '_full'
        processed_dir = Path(args.basedir, f'processed{suffix}')
    else:
        processed_dir = Path(args.basedir, args.outdir)

    processed_dir.mkdir(exist_ok=True, parents=True)

    if args.make_split:
        # Process the label file
        csv_path = args.basedir / 'every.csv'
        ligand_dict = read_label_file(csv_path)
        ligand_dict = compute_druglikeness(ligand_dict)
        filtered_examples = filter_and_flatten(
            ligand_dict, args.qed_thresh, args.max_occurences, args.random_seed)
        print(f'{len(filtered_examples)} examples after filtering')

        # Make data split
        data_split = split_by_ec_number(filtered_examples, args.num_val,
                                        args.num_test)

    else:
        # Use precomputed data split
        data_split = {}
        for split in ['test', 'val', 'train']:
            with open(f'data/moad_{split}.txt', 'r') as f:
                pocket_ids = f.read().split(',')
            # (ec-number, protein, molecule tuple)
            data_split[split] = [(None, x.split('_')[0][:4], (x.split('_')[1],))
                          for x in pocket_ids]

    n_train_before = len(data_split['train'])
    n_val_before = len(data_split['val'])
    n_test_before = len(data_split['test'])

    # Read and process PDB files
    index = {'train': [], 'val': [], 'test': []}
    n_samples_after = {}
    for split in data_split.keys():
        lig_coords = []
        lig_one_hot = []
        lig_mask = []
        pocket_coords = []
        pocket_one_hot = []
        pocket_mask = []
        pdb_and_mol_ids = []
        receptors = []
        count = 0

        pdb_sdf_dir = processed_dir
        pdb_sdf_dir.mkdir(exist_ok=True)

        n_tot = len(data_split[split])
        pair_dict = ligand_list_to_dict(data_split[split])

        tic = time()
        num_failed = 0
        with tqdm(total=n_tot) as pbar:
            for p in pair_dict:

                pdb_successful = set()

                # try all available .bio files
                for pdbfile in sorted(pdbdir.glob(f"{p.lower()}.bio*")):

                    # Skip if all ligands have been processed already
                    if len(pair_dict[p]) == len(pdb_successful):
                        continue

                    pdb_struct = PDBParser(QUIET=True).get_structure('', pdbfile)
                    struct_copy = pdb_struct.copy()

                    n_bio_successful = 0
                    for m in pair_dict[p]:

                        # Skip already processed ligand
                        if m[0] in pdb_successful:
                            continue

                        ligand_name, ligand_chain, ligand_resi = m[0].split(':')
                        ligand_resi = int(ligand_resi)

                        try:
                            ligand_data, pocket_data = process_ligand_and_pocket(
                                pdb_struct, ligand_name, ligand_chain, ligand_resi,
                                dist_cutoff=args.dist_cutoff, ca_only=args.ca_only)
                        except (KeyError, AssertionError, FileNotFoundError,
                                IndexError, ValueError) as e:
                            # print(type(e).__name__, e)
                            continue

                        pdb_and_mol_ids.append(f"{p}_{m[0]}")
                        receptors.append(pdbfile.name)
                        lig_coords.append(ligand_data['lig_coords'])
                        lig_one_hot.append(ligand_data['lig_one_hot'])
                        lig_mask.append(
                            count * np.ones(len(ligand_data['lig_coords'])))
                        pocket_coords.append(pocket_data['pocket_coords'])
                        # pocket_quaternion.append(
                        #     pocket_data['pocket_quaternion'])
                        pocket_one_hot.append(pocket_data['pocket_one_hot'])
                        pocket_mask.append(
                            count * np.ones(len(pocket_data['pocket_coords'])))
                        count += 1

                        pdb_successful.add(m[0])
                        n_bio_successful += 1

                        # Save additional files for affinity analysis
                        if split in {'val', 'test', 'train'}:
                            # remove ligand from receptor
                            try:
                                struct_copy[0][ligand_chain].detach_child((f'H_{ligand_name}', ligand_resi, ' '))
                            except KeyError:
                                warnings.warn(f"Could not find ligand {(f'H_{ligand_name}', ligand_resi, ' ')} in {pdbfile}")
                                continue

                            # Create SDF file
                            atom_types = [atom_decoder[np.argmax(i)] for i in ligand_data['lig_one_hot']]
                            xyz_file = Path(pdb_sdf_dir, 'tmp.xyz')
                            moad_utils.write_xyz_file(ligand_data['lig_coords'], atom_types, xyz_file)

                            obConversion = openbabel.OBConversion()
                            obConversion.SetInAndOutFormats("xyz", "sdf")
                            mol = openbabel.OBMol()
                            obConversion.ReadFile(mol, str(xyz_file))
                            xyz_file.unlink()

                            lig_name = f"{p}-{pdbfile.suffix[1:]}_{m[0]}"
                            sdf_file = Path(pdb_sdf_dir, f'{lig_name}.sdf')
                            obConversion.WriteFile(mol, str(sdf_file))
                            pdb_name = f'{p}-{pdbfile.suffix[1:]}.pdb'

                            index[split].append((pdb_name, lig_name))

                            # specify pocket residues
                            #with open(Path(pdb_sdf_dir, f'{name}.txt'), 'w') as f:
                                #f.write(' '.join(pocket_data['pocket_ids']))

                    if split in {'val', 'test', 'train'} and n_bio_successful > 0:
                        # create receptor PDB file
                        pdb_file_out = Path(pdb_sdf_dir, f'{p}-{pdbfile.suffix[1:]}.pdb')
                        io = PDBIO()
                        io.set_structure(struct_copy)
                        io.save(str(pdb_file_out), select=Model0())

                pbar.update(len(pair_dict[p]))
                num_failed += (len(pair_dict[p]) - len(pdb_successful))
                pbar.set_description(f'#failed: {num_failed}')


        lig_coords = np.concatenate(lig_coords, axis=0)
        lig_one_hot = np.concatenate(lig_one_hot, axis=0)
        lig_mask = np.concatenate(lig_mask, axis=0)
        pocket_coords = np.concatenate(pocket_coords, axis=0)
        pocket_one_hot = np.concatenate(pocket_one_hot, axis=0)
        pocket_mask = np.concatenate(pocket_mask, axis=0)

        np.savez(processed_dir / f'{split}.npz', names=pdb_and_mol_ids,
                 receptors=receptors, lig_coords=lig_coords,
                 lig_one_hot=lig_one_hot, lig_mask=lig_mask,
                 pocket_coords=pocket_coords, pocket_one_hot=pocket_one_hot,
                 pocket_mask=pocket_mask)

        n_samples_after[split] = len(pdb_and_mol_ids)
        print(f"Processing {split} set took {(time() - tic)/60.0:.2f} minutes")

    index_path = os.path.join('./data/bindingmoad/all_data', 'index.pkl')
    with open(index_path, 'wb') as f:
        pickle.dump(index, f)

    # --------------------------------------------------------------------------
    # Compute statistics & additional information
    # --------------------------------------------------------------------------
    with np.load(processed_dir / 'train.npz', allow_pickle=True) as data:
        lig_mask = data['lig_mask']
        pocket_mask = data['pocket_mask']
        lig_coords = data['lig_coords']
        lig_one_hot = data['lig_one_hot']
        pocket_one_hot = data['pocket_one_hot']

    # Compute SMILES for all training examples
    train_smiles = compute_smiles(lig_coords, lig_one_hot, lig_mask)
    np.save(processed_dir / 'train_smiles.npy', train_smiles)

    # Joint histogram of number of ligand and pocket nodes
    n_nodes = get_n_nodes(lig_mask, pocket_mask, smooth_sigma=1.0)
    np.save(Path(processed_dir, 'size_distribution.npy'), n_nodes)

    # Convert bond length dictionaries to arrays for batch processing
    bonds1, bonds2, bonds3 = get_bond_length_arrays(atom_dict)

    # Get bond length definitions for Lennard-Jones potential
    rm_LJ = get_lennard_jones_rm(atom_dict)

    # Get histograms of ligand and pocket node types
    atom_hist, aa_hist = get_type_histograms(lig_one_hot, pocket_one_hot,
                                             atom_dict, amino_acid_dict)

    # Create summary string
    summary_string = '# SUMMARY\n\n'
    summary_string += '# Before processing\n'
    summary_string += f'num_samples train: {n_train_before}\n'
    summary_string += f'num_samples val: {n_val_before}\n'
    summary_string += f'num_samples test: {n_test_before}\n\n'
    summary_string += '# After processing\n'
    summary_string += f"num_samples train: {n_samples_after['train']}\n"
    summary_string += f"num_samples val: {n_samples_after['val']}\n"
    summary_string += f"num_samples test: {n_samples_after['test']}\n\n"
    summary_string += '# Info\n'
    summary_string += f"'atom_encoder': {atom_dict}\n"
    summary_string += f"'atom_decoder': {list(atom_dict.keys())}\n"
    summary_string += f"'aa_encoder': {amino_acid_dict}\n"
    summary_string += f"'aa_decoder': {list(amino_acid_dict.keys())}\n"
    summary_string += f"'bonds1': {bonds1.tolist()}\n"
    summary_string += f"'bonds2': {bonds2.tolist()}\n"
    summary_string += f"'bonds3': {bonds3.tolist()}\n"
    summary_string += f"'lennard_jones_rm': {rm_LJ.tolist()}\n"
    summary_string += f"'atom_hist': {atom_hist}\n"
    summary_string += f"'aa_hist': {aa_hist}\n"
    summary_string += f"'n_nodes': {n_nodes.tolist()}\n"

    # Write summary to text file
    with open(processed_dir / 'summary.txt', 'w') as f:
        f.write(summary_string)

    # Print summary
    print(summary_string)