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generate_pdbbind_graphs.py
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generate_pdbbind_graphs.py
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import pandas as pd
import pickle
import torch
import torchani
import torchani_mod
import qcelemental as qcel
import numpy as np
from tqdm import tqdm
import os
from rdkit import Chem
def elements_to_atomicnums(elements):
"""
Convert element symbols to atomic numbers.
Parameters
----------
elements: Iterable
Iterable object with lement symbols
Returns
-------
np.ndarray
Array of atomic numbers
"""
atomicnums = np.zeros(len(elements), dtype=int)
for idx, e in enumerate(elements):
atomicnums[idx] = qcel.periodictable.to_Z(e)
return atomicnums
def LoadMolasDF(mol):
# This function converts the input ligand (.MOL file) into a pandas DataFrame with the ligand atom position in 3D (X,Y,Z)
# load the ligand
m = mol
atoms = []
for atom in m.GetAtoms():
if atom.GetSymbol() != "H": # Include only non-hydrogen atoms
entry = [int(atom.GetIdx())]
entry.append(str(atom.GetSymbol()))
pos = m.GetConformer().GetAtomPosition(atom.GetIdx())
entry.append(float("{0:.4f}".format(pos.x)))
entry.append(float("{0:.4f}".format(pos.y)))
entry.append(float("{0:.4f}".format(pos.z)))
atoms.append(entry)
df = pd.DataFrame(atoms)
df.columns = ["ATOM_INDEX","ATOM_TYPE","X","Y","Z"]
return df
def LoadPDBasDF(PDB, atom_keys):
# This function converts a protein PDB file into a pandas DataFrame with the protein atom position in 3D (X,Y,Z)
prot_atoms = []
f = open(PDB)
for i in f:
if i[:4] == "ATOM":
# Include only non-hydrogen atoms
if (len(i[12:16].replace(" ","")) < 4 and i[12:16].replace(" ","")[0] != "H") or (len(i[12:16].replace(" ","")) == 4 and i[12:16].replace(" ","")[1] != "H" and i[12:16].replace(" ","")[0] != "H"):
prot_atoms.append([int(i[6:11]),
i[17:20]+"-"+i[12:16].replace(" ",""),
float(i[30:38]),
float(i[38:46]),
float(i[46:54])
])
f.close()
df = pd.DataFrame(prot_atoms, columns=["ATOM_INDEX","PDB_ATOM","X","Y","Z"])
df = df.merge(atom_keys, left_on='PDB_ATOM', right_on='PDB_ATOM')[["ATOM_INDEX", "ATOM_TYPE", "X", "Y", "Z"]].sort_values(by="ATOM_INDEX").reset_index(drop=True)
if list(df["ATOM_TYPE"].isna()).count(True) > 0:
print("WARNING: Protein contains unsupported atom types. Only supported atom-type pairs are counted.")
return df
def GetMolAEVs_extended(protein_path, mol, atom_keys, radial_coefs, atom_map):
"""
Calculate AEVs for molecule atoms based on protein atoms in a complex.
Hydrogens in both the molecule and protein are ignored and behind the
curtains molecule atoms are encoded as carbon to use torchani_mod code.
Parameters
----------
protein_path: string, path to protein file (.pdb)
mol_path: string, path to molecule file (.mol2)
atom_keys: dataframe of atom types in proteins
radial_coefs: list of pytorch tensors of RcR, EtaR, RsR
angular_coefs: list of pytorch tensors of RcA, Zeta, TsA, EtaA, RsA
Returns
-------
Tensor of shape [1, A, L], where A is the number of atoms and L is the
appropriate length of AEV.
Todo
----
Assert that RcR > RcA
"""
# Protein and ligand structure are loaded as pandas DataFrame
Target = LoadPDBasDF(protein_path, atom_keys)
Ligand = LoadMolasDF(mol)
# Define AEV coeficients
# Radial coefficients
RcR = radial_coefs[0]
EtaR = radial_coefs[1]
RsR = radial_coefs[2]
# Angular coefficients (Ga)
RcA = 2.0
Zeta = torch.tensor([1.0])
TsA = torch.tensor([1.0]) # Angular shift in GA
EtaA = torch.tensor([1.0])
RsA = torch.tensor([1.0]) # Radial shift in GA
# Reduce size of Target df to what we need based on radial cutoff RcR
distance_cutoff = RcR + 0.1
for i in ["X","Y","Z"]:
Target = Target[Target[i] < float(Ligand[i].max())+distance_cutoff]
Target = Target[Target[i] > float(Ligand[i].min())-distance_cutoff]
Target = Target.merge(atom_map, on='ATOM_TYPE', how='left')
# Create tensors of atomic numbers and coordinates of molecule atoms and
# protein atoms combined. Encode molecule atoms as hydrogen
mol_len = torch.tensor(len(Ligand))
atomicnums = np.append(np.ones(mol_len)*6, Target["ATOM_NR"])
atomicnums = torch.tensor(atomicnums, dtype=torch.int64)
atomicnums = atomicnums.unsqueeze(0)
coordinates = pd.concat([Ligand[['X','Y','Z']], Target[['X','Y','Z']]])
coordinates = torch.tensor(coordinates.values)
coordinates = coordinates.unsqueeze(0)
# Use torchani_mod to calculate AEVs
atom_symbols = []
for i in range(1, 23):
atom_symbols.append(qcel.periodictable.to_symbol(i))
AEVC = torchani_mod.AEVComputer(RcR, RcA, EtaR, RsR,
EtaA, Zeta, RsA, TsA, len(atom_symbols))
SC = torchani.SpeciesConverter(atom_symbols)
sc = SC((atomicnums, coordinates))
aev = AEVC.forward((sc.species, sc.coordinates), mol_len)
# find indices of columns to keep
# keep all radial terms
n = len(atom_symbols)
n_rad_sub = len(EtaR)*len(RsR)
indices = list(np.arange(n*n_rad_sub))
return Ligand, aev.aevs.squeeze(0)[:mol_len,indices]
def GetMolAEVs_rad(protein_path, mol, atom_keys, radial_coefs):
"""
Calculate AEVs for molecule atoms based on protein atoms in a complex.
Hydrogens in both the molecule and protein are ignored and behind the
curtains molecule atoms are encoded as carbon to use torchani_mod code.
Parameters
----------
protein_path: string, path to protein file (.pdb)
mol_path: string, path to molecule file (.mol2)
atom_keys: dataframe of atom types in proteins
radial_coefs: list of pytorch tensors of RcR, EtaR, RsR
angular_coefs: list of pytorch tensors of RcA, Zeta, TsA, EtaA, RsA
Returns
-------
Tensor of shape [1, A, L], where A is the number of atoms and L is the
appropriate length of AEV.
Todo
----
Assert that RcR > RcA
"""
# Protein and ligand structure are loaded as pandas DataFrame
Target = LoadPDBasDF(protein_path, atom_keys)
Ligand = LoadMolasDF(mol)
# Define AEV coeficients
# Radial coefficients
RcR = radial_coefs[0]
EtaR = radial_coefs[1]
RsR = radial_coefs[2]
# Angular coefficients (Ga)
RcA = 2.0
Zeta = torch.tensor([1.0])
TsA = torch.tensor([1.0]) # Angular shift in GA
EtaA = torch.tensor([1.0])
RsA = torch.tensor([1.0]) # Radial shift in GA
# Reduce size of Target df to what we need based on radial cutoff RcR
distance_cutoff = RcR + 0.1
for i in ["X","Y","Z"]:
Target = Target[Target[i] < float(Ligand[i].max())+distance_cutoff]
Target = Target[Target[i] > float(Ligand[i].min())-distance_cutoff]
# Instead of all these different atom types we only use atom symbol
Target["ATOM_SYMBOL"] = [i.split(";")[0] for i in Target["ATOM_TYPE"]]
# Create tensors of atomic numbers and coordinates of molecule atoms and
# protein atoms combined. Encode molecule atoms as hydrogen
mol_len = torch.tensor(len(Ligand))
atomicnums = np.append(np.ones(mol_len)*6, elements_to_atomicnums(Target['ATOM_SYMBOL']))
atomicnums = torch.tensor(atomicnums, dtype=torch.int64)
atomicnums = atomicnums.unsqueeze(0)
coordinates = pd.concat([Ligand[['X','Y','Z']], Target[['X','Y','Z']]])
coordinates = torch.tensor(coordinates.values)
coordinates = coordinates.unsqueeze(0)
# Use torchani_mod to calculate AEVs
atom_symbols = ['C','N','O','S']
AEVC = torchani_mod.AEVComputer(RcR, RcA, EtaR, RsR,
EtaA, Zeta, RsA, TsA, len(atom_symbols))
SC = torchani.SpeciesConverter(atom_symbols)
sc = SC((atomicnums, coordinates))
aev = AEVC.forward((sc.species, sc.coordinates), mol_len)
# find indices of columns to keep
# keep all radial terms
n = len(atom_symbols)
n_rad_sub = len(EtaR)*len(RsR)
indices = list(np.arange(n*n_rad_sub))
return Ligand, aev.aevs.squeeze(0)[:mol_len,indices]
def one_of_k_encoding(x, allowable_set):
"""
taken from https://github.com/thinng/GraphDTA
function which one hot encodes x w.r.t. allowable_set and x has to be in allowable_set
x:
element from allowable_set
allowable_set: list
list of elements x is from
"""
if x not in allowable_set:
raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set))
return list(map(lambda s: x == s, allowable_set))
def atom_features(atom, features=["atom_symbol",
"num_heavy_atoms",
"total_num_Hs",
"explicit_valence",
"is_aromatic",
"is_in_ring"]):
# Computes the ligand atom features for graph node construction
# The standard features are the following:
# atom_symbol = one hot encoding of atom symbol
# num_heavy_atoms = # of heavy atom neighbors
# total_num_Hs = # number of hydrogen atom neighbors
# explicit_valence = explicit valence of the atom
# is_aromatic = boolean 1 - aromatic, 0 - not aromatic
# is_in_ring = boolean 1 - is in ring, 0 - is not in ring
feature_list = []
if "atom_symbol" in features:
feature_list.extend(one_of_k_encoding(atom.GetSymbol(),['F', 'N', 'Cl', 'O', 'Br', 'C', 'B', 'P', 'I', 'S']))
if "num_heavy_atoms" in features:
feature_list.append(len([x.GetSymbol() for x in atom.GetNeighbors() if x.GetSymbol() != "H"]))
if "total_num_Hs" in features:
feature_list.append(len([x.GetSymbol() for x in atom.GetNeighbors() if x.GetSymbol() == "H"]))
if "explicit_valence" in features: #-NEW ADDITION FOR PLIG
feature_list.append(atom.GetExplicitValence())
if "is_aromatic" in features:
if atom.GetIsAromatic():
feature_list.append(1)
else:
feature_list.append(0)
if "is_in_ring" in features:
if atom.IsInRing():
feature_list.append(1)
else:
feature_list.append(0)
return np.array(feature_list)
def mol_to_graph(mol, mol_df, aevs, extra_features=["atom_symbol",
"num_heavy_atoms",
"total_num_Hs",
"explicit_valence",
"is_aromatic",
"is_in_ring"]):
features = []
heavy_atom_index = []
idx_to_idx = {}
counter = 0
# Generate nodes
for atom in mol.GetAtoms():
if atom.GetSymbol() != "H": # Include only non-hydrogen atoms
idx_to_idx[atom.GetIdx()] = counter
aev_idx = mol_df[mol_df['ATOM_INDEX'] == atom.GetIdx()].index
heavy_atom_index.append(atom.GetIdx())
feature = np.append(atom_features(atom), aevs[aev_idx,:])
features.append(feature)
counter += 1
#Generate edges
edges = []
for bond in mol.GetBonds():
idx1 = bond.GetBeginAtomIdx()
idx2 = bond.GetEndAtomIdx()
if idx1 in heavy_atom_index and idx2 in heavy_atom_index:
bond_type = one_of_k_encoding(bond.GetBondType(),[1,12,2,3])
bond_type = [float(b) for b in bond_type]
edge1 = [idx_to_idx[idx1], idx_to_idx[idx2]]
edge1.extend(bond_type)
edge2 = [idx_to_idx[idx2], idx_to_idx[idx1]]
edge2.extend(bond_type)
edges.append(edge1)
edges.append(edge2)
df = pd.DataFrame(edges, columns=['atom1', 'atom2', 'single', 'aromatic', 'double', 'triple'])
df = df.sort_values(by=['atom1','atom2'])
edge_index = df[['atom1','atom2']].to_numpy().tolist()
edge_attr = df[['single','aromatic','double','triple']].to_numpy().tolist()
return len(mol_df), features, edge_index, edge_attr
'''
Load data
'''
data = pd.read_csv("data/pdbbind_processed.csv", index_col=0)
'''
Generate for all complexes: ANI-2x with 22 atom types. Only 2-atom interactions
'''
print("The number of data points is ", len(data))
atom_keys = pd.read_csv("data/PDB_Atom_Keys.csv", sep=",")
atom_map = pd.DataFrame(pd.unique(atom_keys["ATOM_TYPE"]))
atom_map[1] = list(np.arange(len(atom_map)) + 1)
atom_map = atom_map.rename(columns={0:"ATOM_TYPE", 1:"ATOM_NR"})
# Radial coefficients: ANI-2x
RcR = 5.1 # Radial cutoff
EtaR = torch.tensor([19.7]) # Radial decay
RsR = torch.tensor([0.80, 1.07, 1.34, 1.61, 1.88, 2.14, 2.41, 2.68,
2.95, 3.22, 3.49, 3.76, 4.03, 4.29, 4.56, 4.83]) # Radial shift
radial_coefs = [RcR, EtaR, RsR]
mol_graphs = {}
failed_list = []
failed_after_reading = []
for i, pdb in tqdm(enumerate(data["PDB_code"])):
if data["refined"][i]:
folder = "data/pdbbind/refined-set/"
else:
folder = "data/pdbbind/general-set/"
mol_path = os.path.join(folder, pdb, f'{pdb}_ligand.mol2')
mol = Chem.MolFromMol2File(mol_path)
if mol is None:
print("can't read molecule structure:", pdb)
failed_list.append(pdb)
continue
else:
mol = Chem.AddHs(mol, addCoords=True)
try:
protein_path = os.path.join(folder, pdb, f'{pdb}_protein.pdb')
mol_df, aevs = GetMolAEVs_extended(protein_path, mol, atom_keys, radial_coefs, atom_map)
graph = mol_to_graph(mol, mol_df, aevs)
mol_graphs[pdb] = graph
except ValueError as e:
print(e)
failed_after_reading.append(pdb)
continue
print(len(failed_list), len(failed_after_reading))
#save the graphs to use as input for the GNN models
output_file_graphs = "data/pdbbind.pickle"
with open(output_file_graphs, 'wb') as handle:
pickle.dump(mol_graphs, handle, protocol=pickle.HIGHEST_PROTOCOL)