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plif_cnn_test_2.py
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#!/usr/bin/env python
# coding: utf-8
import os
import sys
import re
import torch
from glob import glob
from openbabel import pybel
ob = pybel.ob
import numpy as np
import random
import pickle
import warnings
import requests
import os
import glob
import pandas as pd
import openbabel
import numpy as np
from operator import add
from plip.structure.preparation import PDBComplex
from plip.exchange.report import BindingSiteReport
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Descriptors3D
from rdkit.Chem import Descriptors
from rdkit.Chem import MACCSkeys
from biopandas.pdb import PandasPdb
from Bio.PDB.SASA import ShrakeRupley
from Bio.PDB import PDBParser
from sklearn.model_selection import train_test_split
from dask.dataframe import from_pandas
from dask.multiprocessing import get
from pandarallel import pandarallel
pandarallel.initialize(progress_bar=False, nb_workers=250,use_memory_fs=False)
df_plifSpecs=pd.DataFrame()
PYTORCH_ENABLE_MPS_FALLBACK=1
PYTORCH_ENABLE_SparseCPU_FALLBACK=1
warnings.filterwarnings("ignore")
from dask_jobqueue import SLURMCluster
train_grids=None
test_grids=None
rotations=19
full_batch=20
features_shape=628
# First training grids:
train_label=[]
# Second testing grids:
test_label=[]
des=dir(Descriptors)
des=[x for x in des if not x.startswith('AUTOCOR')]
des=[x for x in des if not x.startswith('BCUT2D')]
des=[x for x in des if not x.startswith('Chi')]
des=[x for x in des if not x.startswith('EState')]
des=[x for x in des if not x.startswith('Chem')]
#des=[x for x in des if not x.startswith('PEOE')]
des=[x for x in des if not x.startswith('SlogP_')]
des=[x for x in des if not x.startswith('__')]
des=[x for x in des if not x.startswith('VSA_')]
des=[x for x in des if not x.startswith('descList')]
des=[x for x in des if not x.startswith('autocorr')]
des=[x for x in des if not x.startswith('abc')]
des=[x for x in des if not x.startswith('_')]
des=[x for x in des if not x.startswith('SMR')]
des=[x for x in des if not x.startswith('PropertyFunctor')]
des=[x for x in des if not x.startswith('names')]
des=[x for x in des if not x.startswith('setupAUTOCorrDescriptors')]
des=[x for x in des if not x.startswith('rdPartialCharges')]
des=[x for x in des if not x.startswith('rdMolDescriptors')]
#des=[x for x in des if not x.startswith('BalabanJ')]
#des=[x for x in des if not x.startswith('BertzCT')]
des=["Descriptors."+ item for item in des]
FragID={}
# cluster = SLURMCluster(cores=1024,
# processes=12,
# memory="250GB",
# # account="hmslati",
# # walltime="01:00:00",
# queue="gpu-bigmem")
# PLEASE READ -> 45次实验分别进行10倍交叉验证，取平均
#Converts the protein-ligand complexes into 4D tensor.
class Feature_extractor():
def __init__(self):
self.res_types=['MET','THR','VAL','PRO','ASP',
'ARG','SER','GLU','ILE','ALA',
'GLY','LYS','TRP','TYR','LEU',
'ASN','PHE','CYS','HIS','GLN']
self.aa_atoms=[' N ', ' HN1', ' HN2', ' HN3', ' CA ', ' HA ', ' C ', ' O ',
' CB ', ' HB1', ' HB2', ' CG ', ' HG1', ' HG2', ' SD ', ' CE ', ' HE1',
' HE2', ' HE3', ' H ', ' HB ', ' OG1', ' CG2', '1HG2', '2HG2', '3HG2',
' CG1', '1HG1', '2HG1', '3HG1', ' CD ', ' HD1', ' HD2', ' OD1', ' OD2',
' NE ', ' HE ', ' CZ ', ' NH1', '1HH1', '2HH1', ' NH2', '1HH2', '2HH2',
' OG ', ' HG ', ' OE1', ' OE2', ' CD1', '1HD1', '2HD1', '3HD1', ' HB3',
' HA1', ' HA2', ' NZ ', ' HZ1', ' HZ2', ' HZ3', ' CD2', ' NE1', ' CE2',
' CE3', ' CZ2', ' CZ3', ' CH2', ' HH2', ' CE1', ' OH ', ' HH ', '1HD2',
'2HD2', '3HD2', ' ND2', ' HZ ', ' SG ', ' ND1', ' NE2', '1HE2', '2HE2', ' OXT']
self.atom_codes = {}
# 89 to 96 will be reserved to PLIF features as follows:
# 89: hydrophobic
# 90: hbond
# 91: waterbridge
# 92: saltbridge
# 93: pistacking
# 94: pication
# 95: halogen
# 96: metal
# 97-116: amino acid memberships
# 117-197: amino acid atom types
# 198-364: fragment mebership
# rarely occuring elements: ['4', '13', '22-23', '25', '28', '32', '40', '45', '47-52', '55', '75-76', '78', '80', '82']
others = ([4,13,25,28,32,40,45,55,78,80,82]+list(range(22,24))+list(range(47,53))+list(range(75,78)))
plif_specs=list(range(89,97))
aa_specs=list(range(97,117))
self.aa_specs_dic = dict(map(lambda i,j : (i,j) , self.res_types,aa_specs))
aa_atom_types=list(range(117,198))
self.aa_atoms_dic = dict(map(lambda i,j : (i,j) , self.aa_atoms,aa_atom_types))
self.frag_mem=list(range(198,365))
#C and N atoms can be hybridized in three ways and S atom can be hybridized in two ways here.
#Hydrogen atom is also considered for feature extraction. I think phosphor atom has 3 or 5 as hyb states but
# in biological system its usually the same recurrent phosphate even in most small molecules so safe to assume one
# hybridization state for this purpose.
atom_types = [1,(6,1),(6,2),(6,3),(7,1),(7,2),(7,3),8,15,(16,2),(16,3),
34,9,17,35,53,11,12,13,14,5,19,20,25,29,28,30,33,3,27,24,26,31,42,79,44,74,others]+plif_specs+ \
aa_specs+aa_atom_types+self.frag_mem
for i, j in enumerate(atom_types):
if type(j) is list:
for k in j:
self.atom_codes[k] = i
else:
self.atom_codes[j] = i
self.sum_atom_types = len(atom_types)
#Onehot encoding of each atom. The atoms in protein or ligand are treated separately.
def encode(self, atomic_num, orig_coords, plifs, molprotein, res=None, aa_atom=None, frag=None):
encoding = np.zeros(self.sum_atom_types*2)
if molprotein == 1:
encoding[self.atom_codes[atomic_num]] = 1.0
encoding[self.atom_codes[res]] = 1.0
encoding[self.atom_codes[aa_atom]] = 1.0
for coord, plif_feats in plifs.items():
if [round(item) for item in coord] == [round(item) for item in orig_coords]:
encoding[self.atom_codes[89]] = 1.0 if plifs[coord][0] == 'hydrophobic' \
else 0.0
encoding[self.atom_codes[90]] = 1.0 if plifs[coord][0] == 'hbond' \
else 0.0
encoding[self.atom_codes[91]] = 1.0 if plifs[coord][0] == 'waterbridge' \
else 0.0
encoding[self.atom_codes[92]] = 1.0 if plifs[coord][0] == 'saltbridge' \
else 0.0
encoding[self.atom_codes[93]] = 1.0 if plifs[coord][0] == 'pistacking' \
else 0.0
encoding[self.atom_codes[94]] = 1.0 if plifs[coord][0] == 'pication' \
else 0.0
encoding[self.atom_codes[95]] = 1.0 if plifs[coord][0] == 'halogen' \
else 0.0
encoding[self.atom_codes[96]] = 1.0 if plifs[coord][0] == 'metal' \
else 0.0
# #distance
# encoding[self.atom_codes[97]] = plifs[coord][1]
else:
encoding[self.sum_atom_types+self.atom_codes[atomic_num]] = 1.0
for coord, plif_feats in plifs.items():
if [round(item) for item in coord] == [round(item) for item in orig_coords]:
encoding[self.sum_atom_types+self.atom_codes[89]] = 1.0 if plifs[coord][0] == 'hydrophobic' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[90]] = 1.0 if plifs[coord][0] == 'hbond' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[91]] = 1.0 if plifs[coord][0] == 'waterbridge' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[92]] = 1.0 if plifs[coord][0] == 'saltbridge' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[93]] = 1.0 if plifs[coord][0] == 'pistacking' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[94]] = 1.0 if plifs[coord][0] == 'pication' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[95]] = 1.0 if plifs[coord][0] == 'halogen' \
else 0.0
encoding[self.sum_atom_types+self.atom_codes[96]] = 1.0 if plifs[coord][0] == 'metal' \
else 0.0
# #distance
# encoding[self.sum_atom_types+self.atom_codes[97]] = plifs[coord][1]
for ndx, coord_list in enumerate([*frag]):
if any(key == tuple(round(item) for item in orig_coords) for key in [tuple(round(y) for y in x)
for x in coord_list]):
encoding[self.sum_atom_types+self.atom_codes[self.frag_mem[0]]-1:-1]=(frag[coord_list][ndx])
return encoding
#Get atom coords and atom features from the complexes.
def get_features(self, molecule, plifs, molprotein, frags=None):
coords = []
features = []
obmol = molecule.OBMol
for res in ob.OBResidueIter(obmol):
for atom in ob.OBResidueAtomIter(res):
coords.append([atom.GetX(),atom.GetY(),atom.GetZ()])
if atom.GetAtomicNum() in [6,7,16]:
atomicnum = (atom.GetAtomicNum(),atom.GetHyb())
features.append(self.encode(atomicnum,(atom.GetX(),atom.GetY(),atom.GetZ()),plifs,molprotein,
self.aa_specs_dic[res.GetName()] if not res.IsHetAtom(atom)
else None, self.aa_atoms_dic[res.GetAtomID(atom)]
if not res.IsHetAtom(atom) else None,
frags))
else:
features.append(self.encode(atom.GetAtomicNum(),(atom.GetX(),atom.GetY(),atom.GetZ()),
plifs,molprotein,self.aa_specs_dic[res.GetName()]
if not res.IsHetAtom(atom) else None,
self.aa_atoms_dic[res.GetAtomID(atom)]
if not res.IsHetAtom(atom) else None,frags))
coords = np.array(coords, dtype=np.float32)
features = np.array(features, dtype=np.float32)
return coords, features
#Define the rotation matrixs of 3D stuctures.
def rotation_matrix(self, t, roller):
if roller==0:
return np.array([[1,0,0],[0,np.cos(t),np.sin(t)],[0,-np.sin(t),np.cos(t)]])
elif roller==1:
return np.array([[np.cos(t),0,-np.sin(t)],[0,1,0],[np.sin(t),0,np.cos(t)]])
elif roller==2:
return np.array([[np.cos(t),np.sin(t),0],[-np.sin(t),np.cos(t),0],[0,0,1]])
#Generate 3d grid or 4d tensor. Each grid represents a voxel. Each voxel represents the atom in it by onehot encoding of atomic type.
#Each complex in train set is rotated 9 times for data amplification.
#The complexes in core set are not rotated.
#The default resolution is 20*20*20.
def grid(self,coords, features, resolution=1.0, max_dist=10.0, rotations=19):
assert coords.shape[1] == 3
assert coords.shape[0] == features.shape[0]
grid=torch.zeros((rotations+1,20,20,20,features_shape))
x=y=z=np.array(range(-10,10),dtype=np.float32)+0.5
for i in range(len(coords)):
coord=coords[i]
tmpx=abs(coord[0]-x)
tmpy=abs(coord[1]-y)
tmpz=abs(coord[2]-z)
if np.max(tmpx)<=19.5 and np.max(tmpy)<=19.5 and np.max(tmpz) <=19.5:
grid[0,np.argmin(tmpx),np.argmin(tmpy),np.argmin(tmpz)] += features[i]
for j in range(rotations):
theta = random.uniform(np.pi/18,np.pi/2)
roller = random.randrange(3)
coords = np.dot(coords, self.rotation_matrix(theta,roller))
for i in range(len(coords)):
coord=coords[i]
tmpx=abs(coord[0]-x)
tmpy=abs(coord[1]-y)
tmpz=abs(coord[2]-z)
if np.max(tmpx)<=19.5 and np.max(tmpy)<=19.5 and np.max(tmpz) <=19.5:
grid[j+1,np.argmin(tmpx),np.argmin(tmpy),np.argmin(tmpz)] += features[i]
return grid
class PLIF:
def __init__(self, PDB: str, MOL_SPLIT_START: int = 70, **kwargs):
kwargs.setdefault('aggr', 'add')
super(PLIF,self).__init__()
self.MOL_SPLIT_START=MOL_SPLIT_START
self.pdb=PDB
self.records=['ATOM']
self.values=['HOH','CL','MG','ZN','MN','CA']
self.ions=['CL','MG','ZN','MN','CA']
self.interaction_slices={"hydrophobic":[0,1,6,7,8,9,10],
"hbond":[0,1,7,11,13,15,16],
"waterbridge":[0,1,[6,7],11,13,16,17],
"saltbridge":[0,1,7,10,3,11,12],
"pistacking":[0,1,7,11,6,12,13],
"pication":[0,1,7,11,3,12,13],
"halogen":[0,1,7,10,12,14,15],
"metal":[0,1,11,8,6,17,16]}
self.column_names = ['RESNR', 'RESTYPE', 'DIST', 'LIG_IDX','PROT_IDX','LIG_COORDS', 'PROT_COORDS']
self.path = os.getcwd()
def okToBreak(self, bond):
"""
Here we apply a bunch of rules to judge if the bond is OK to break.
Parameters
----------
bond :
RDkit MOL object
Returns
-------
Boolean :
OK or not to break.
"""
# See if the bond is in Ring (don't break that)
if bond.IsInRing():
return False
# We OK only single bonds to break
if bond.GetBondType() != Chem.rdchem.BondType.SINGLE:
return False
# Get the beginning atom of the bond
begin_atom = bond.GetBeginAtom()
# Get the ending atom of the bond
end_atom = bond.GetEndAtom()
# What kind of neighbors does these end and begenning atoms have? We need a family of no less than 5!
neighbor_end=list(end_atom.GetNeighbors())
neighbor_begin=list(begin_atom.GetNeighbors())
if (len(neighbor_end) + len(neighbor_begin)) <5:
return False
#for atm in neighbor_end:
#print(atm.GetAtomicNum())
#print(begin_atom.GetAtomicNum(), end_atom.GetAtomicNum(), MOL_SPLIT_START)
# Now check if end or begenning atoms are in ring (we dont wanna bother those)
if not(begin_atom.IsInRing() or end_atom.IsInRing()):
return False
elif begin_atom.GetAtomicNum() >= self.MOL_SPLIT_START or \
end_atom.GetAtomicNum() >= self.MOL_SPLIT_START:
return False
elif end_atom.GetAtomicNum() == 1:
return False
else:
return True
def undo_id_label (self, frag, split_id):
# I am trying to restore Hydrogens where the break happened
for i, atom in enumerate(frag.GetAtoms()):
if atom.GetAtomicNum() >= split_id:
atom.SetAtomicNum(1)
return frag
def _3Descriptors(self, mol):
collected_descriptors=[]
collected_descriptors.append(Descriptors3D.Asphericity(mol))
collected_descriptors.append(Descriptors3D.Eccentricity(mol))
collected_descriptors.append(Descriptors3D.InertialShapeFactor(mol))
collected_descriptors.append(Descriptors3D.NPR1(mol))
collected_descriptors.append(Descriptors3D.NPR2(mol))
collected_descriptors.append(Descriptors3D.PMI1(mol))
collected_descriptors.append(Descriptors3D.PMI2(mol))
collected_descriptors.append(Descriptors3D.PMI3(mol))
collected_descriptors.append(Descriptors3D.RadiusOfGyration(mol))
collected_descriptors.append(Descriptors3D.SpherocityIndex(mol))
return collected_descriptors
def FragID_assign(self, mol):
invariantID=AllChem.GetHashedMorganFingerprint(mol,radius=2,nBits=1024)
key=str(''.join([str(item) for item in invariantID]))
try:
return FragID[key]
except:
FragID[key] = len(FragID)+1
return [FragID[key]]
def FragID_assign_2(self, mol):
return list(MACCSkeys.GenMACCSKeys(mol))
def atoms_coords_frags(self, mol):
coords=[]
for idx, atom in enumerate(mol.GetAtoms()):
positions = mol.GetConformer().GetAtomPosition(idx)
coords.append((positions.x, positions.y, positions.z))
return coords
# Divide a molecule into fragments
def split_interact_molecule(self, mol, pdb):
## here we calculate 2d descriptors
descriptors2D=[eval(des[idx])(mol) for idx,i in enumerate(des)]
# we may need to overwrite with rdkit sanitized mol
w = Chem.PDBWriter(f"tmp_HETATM_{pdb}.pdb")
w.write(mol)
w.close()
with open(f"tmp_HETATM_{pdb}.pdb") as f1, open(f"HETATM_{pdb}.pdb", 'w') as f2:
for line in f1:
if ('HETATM' in line) and not any(ion in line for ion in self.ions):
f2.write(line)
f2.close()
split_id = self.MOL_SPLIT_START
res = []
res_no_id=[]
to_check = [mol]
while len(to_check) > 0:
ms = self.spf(to_check.pop(), split_id)
if len(ms) == 1:
res += ms
else:
to_check += ms
split_id += 1
for frag in res:
res_no_id.append(self.undo_id_label(frag, self.MOL_SPLIT_START))
#stop everything if frags exceed 10
#if len(res_no_id) > 15:
# raise Exception(f"sorry does not support large ligands with more than 15 fragments possible")
# here we get the coords for each fragment (this is purely to match later and map to descriptors and memberships):
frag_coords=list(map(self.atoms_coords_frags, res_no_id))
## here we calculate 3d descriptors
descriptors3D=list(map(self._3Descriptors, res_no_id))
## here we get fragment membership
memberships=list(map(self.FragID_assign_2,res_no_id))
#descriptors3D=list(map(add, descriptors3D, memberships))
# Now we index each of the 3D feats into a slot of the allocated 10 fragments (max). If exceed return error.
# i.e. we discard that sample
trail=[0,0,0,0,0,0,0,0,0,0,0]
trail=[trail]*10
for idx, pos in enumerate(zip(trail,descriptors3D)):
trail[idx]=descriptors3D[idx]
descriptors3D=trail
data = data2 = ""
# Reading data from file1
with open(f"ATOM_{pdb}.pdb") as fp:
data = fp.read()
# Reading data from file2
with open(f"HETATM_{pdb}.pdb") as fp:
data2 = fp.read()
data += data2
with open(f"HOH_{pdb}.pdb") as fp:
data3 = fp.read()
data += data3
with open (f"ATOM_{pdb}_clean.pdb", 'w') as fp:
fp.write(data)
return self.interaction_df(f"ATOM_{pdb}_clean.pdb", descriptors2D, descriptors3D, frag_coords, memberships)
# Function for doing all the nitty gritty splitting work.
# loops over bonds until bonds get exhausted or bonds are ok to break, whichever comes first. If ok to break, then each
# fragment needs to be checked individually again through the loop
def spf(self, mol, split_id):
bonds = mol.GetBonds()
for i in range(len(bonds)):
if self.okToBreak(bonds[i]):
mol = Chem.FragmentOnBonds(mol, [i])
# Dummy atoms are always added last
n_at = mol.GetNumAtoms()
print('Split ID', split_id)
mol.GetAtomWithIdx(n_at-1).SetAtomicNum(split_id)
mol.GetAtomWithIdx(n_at-2).SetAtomicNum(split_id)
return Chem.rdmolops.GetMolFrags(mol, asMols=True)
# If the molecule could not been split, return original molecule
return [mol]
#get_fragments(fragment_mols)
def retreive_plip_interactions(self, pdb_file):
"""
Retreives the interactions from PLIP.
Parameters
----------
pdb_file :
The PDB file of the complex.
Returns
-------
dict :
A dictionary of the binding sites and the interactions.
"""
protlig = PDBComplex() #instantiate the loader from PLIP
protlig.load_pdb(pdb_file) # load the pdb file
for ligand in protlig.ligands:
protlig.characterize_complex(ligand) # find ligands and analyze interactions
sites = {}
# loop over binding sites
for key, site in sorted(protlig.interaction_sets.items()):
binding_site = BindingSiteReport(site) # collect data about interactions
# tuples of *_features and *_info will be converted to pandas DataFrame
keys = (
"hydrophobic",
"hbond",
"waterbridge",
"saltbridge",
"pistacking",
"pication",
"halogen",
"metal"
)
# interactions is a dictionary which contains relevant information for each
# of the possible interactions: hydrophobic, hbond, etc. in the considered
# binding site. Each interaction contains a list with
# 1. the features of that interaction, e.g. for hydrophobic:
# ('RES_number', 'RES_type', ..., 'LIG_coord', 'PROT_coord')
# 2. information for each of these features, e.g. for hydrophobic
# ('RES_number', 'RES_type', ..., 'LIG_coord', 'PROT_coord')
interactions = {
k: [getattr(binding_site, k + "_features")] + getattr(binding_site, k + "_info")
for k in keys
}
sites[key] = interactions
return sites
def get_coords_prot(self, RESNR):
ppdb = PandasPdb()
ppdb.read_pdb(f"{self.pdb.split('.')[0]}_protein.pdb")
only_protein=ppdb.df['ATOM']
resnr_coords=[]
for i in RESNR:
resnr_coords.append(list(only_protein[only_protein['atom_number']==int(i)][['x_coord', 'y_coord', 'z_coord']].values[0]))
return resnr_coords
def interaction_df(self, pdb_file, descriptors2D, descriptors3D, frag_coords, memberships):
all_interactions_df = pd.DataFrame()
# We create the dictionary for the complex of interest:
file_id=pdb_file.split('.')[0]
raw=file_id.split('_')[1]
interactions_by_site = self.retreive_plip_interactions(pdb_file)
# Let’s see how many binding sites are detected:
# print(
# f"Number of binding sites detected in {pdb_id} : "
# f"{len(interactions_by_site)}\n"
# f"with {interactions_by_site.keys()}"
# )
# In this case, the first binding site containing ligand 03P will be further investigated.
index_of_selected_site = 0
selected_site = list(interactions_by_site.keys())[index_of_selected_site]
#print(selected_site)
valid_types = [
"hydrophobic",
"hbond",
"waterbridge",
"saltbridge",
"pistacking",
"pication",
"halogen",
"metal",
]
for _type in valid_types:
output_df=self.create_df_from_binding_site(raw, descriptors2D, descriptors3D, frag_coords, memberships,
interactions_by_site[selected_site],
selected_site,
interactions_by_site,
interaction_type=_type)
all_interactions_df=all_interactions_df.append(output_df)
all_interactions_df = all_interactions_df[all_interactions_df['RESNR'].notna()]
all_interactions_df.to_csv(f"{self.path}/results_plifs/{raw}_plifs_and_properties.csv", index=False)
return all_interactions_df
# We can construct a pandas.DataFrame for a binding site and particular interaction type.
def create_df_from_binding_site(self, raw, descriptors2D, descriptors3D, frag_coords, memberships,
selected_site_interactions, selected_site,
interactions_by_site, interaction_type="hbond"):
"""
Creates a data frame from a binding site and interaction type.
Parameters
----------
selected_site_interactions : dict
Precalculated interactions from PLIP for the selected site
interaction_type : str
The interaction type of interest (default set to hydrogen bonding).
Returns
-------
pd.DataFrame :
DataFrame with information retreived from PLIP.
"""
# check if interaction type is valid:
valid_types = [
"hydrophobic",
"hbond",
"waterbridge",
"saltbridge",
"pistacking",
"pication",
"halogen",
"metal",
]
if interaction_type not in valid_types:
print("!!! Wrong interaction type specified. Hbond is chosen by default !!! \n")
interaction_type = "hbond"
def interaction_values(n):
try:
interactions=interactions_by_site[selected_site][interaction_type]
if type(n) is list:
return [interactions[1:][x][i] for x in
range(len(interactions[1:])) for i in n]
else:
return [interactions[1:][x][n] for x in
range(len(interactions[1:]))]
except Exception:
return None
if interactions_by_site[selected_site][interaction_type][1:]:
#print(list(map(interaction_vasues, self.interaction_slices[interaction_type])), self.column_names)
selected_feats=list(map(interaction_values, self.interaction_slices[interaction_type]))
#print(selected_feats)
try:
if int(selected_feats[4])>int(selected_feats[3]):
selected_feats[3], selected_feats[4] = selected_feats[4], selected_feats[3]
except:
if int(any(selected_feats[4]))>int(any(selected_feats[3])):
selected_feats[3], selected_feats[4] = selected_feats[4], selected_feats[3]
df = pd.DataFrame(
# data is stored AFTER the columns names
[selected_feats],
# column names are always the first element - we skipped that in the above - we are gonna use that for naming the df
columns = self.column_names
)
df["INTERACTION_TYPE"]=interaction_type
try:
checked_coords=self.get_coords_prot(selected_feats[4][0].split(',') if ',' in selected_feats[4][0] \
else selected_feats[4])
except:
checked_coords=selected_feats[6]
df["PROT_COORDS"]=[checked_coords]
#[self.get_coords_prot(selected_feats[4].split(','))]
df["LIG_COORDS"]=[selected_feats[5]]
#[self.get_coords_lig(selected_feats[3].split(','))]
df['DESCRIPTORS_2D']=[descriptors2D] #131 features
df['DESCRIPTORS_3D']=[descriptors3D] #10 features x fragments possible
df['MEMBERSHIPS']=[memberships]
df['FRAG_COORDS']=[frag_coords]
df['SAMPLE_ID']=[69]
# ideally we would like to exclude waters from further processing. Threrfore let us reduce any waterbridge
# interaction to the eucladean distance in order to omit water
if interaction_type == "waterbridge":
df['DIST']=[[np.linalg.norm(x) for x in df['DIST'].to_numpy()]]
# also deal with one distance value and two coords, this is common in saltbridge interactions:
if len(checked_coords) == len(selected_feats[2])*2:
df['DIST']=[selected_feats[2] + selected_feats[2]]
else:
df= pd.DataFrame({'RESNR':[None], 'RESTYPE':[None], 'DIST':[None], 'LIG_IDX':[None],'PROT_IDX':[None],
'INTERACTION_TYPE':[interaction_type], "PROT_COORDS": [None], "LIG_COORDS":[None],
'SAMPLE_ID':[69],
'DESCRIPTORS_2D':[descriptors2D], 'DESCRIPTORS_3D':[descriptors3D],
'FRAG_COORDS':[frag_coords], 'MEMBERSHIPS':[memberships]})
return df
def pdb_2_sdf(self, pdb):
obConversion = openbabel.OBConversion()
obConversion.SetInAndOutFormats("pdb", "sdf")
mol = openbabel.OBMol()
obConversion.ReadFile(mol, pdb) # Open Babel will uncompress automatically
mol.AddHydrogens()
obConversion.WriteFile(mol, f"{pdb.split('.')[0]}.sdf")
return f"{pdb.split('.')[0]}.sdf"
def sdf_2_pdb(self, sdf):
obConversion = openbabel.OBConversion()
obConversion.SetInAndOutFormats("sdf", "pdb")
mol = openbabel.OBMol()
obConversion.ReadFile(mol, sdf) # Open Babel will uncompress automatically
mol.AddHydrogens()
obConversion.WriteFile(mol, f"{sdf.split('.')[0]}.pdb")
return f"HETATM_{sdf.split('.')[0]}.pdb"
def save_bpdb(self, pdb,ppdb, record):
ppdb.to_pdb(path=f"{record}_{pdb.split('.')[0].split('_')[0]}.pdb",
records=[record],
gz=False,
append_newline=True)
def get_HOH_pdb(self, pdb):
ppdb = PandasPdb()
ppdb.read_pdb(pdb)
ppdb.df['HETATM']=ppdb.df['HETATM'].loc[ppdb.df['HETATM']['residue_name'].isin(self.values)]
ppdb.to_pdb(path=f"HOH_{pdb.split('.')[0].split('_')[0]}.pdb",
records=['HETATM'],
gz=False,
append_newline=True)
def keep_relevant_hetatm(self, pdb):
raw=str(self.pdb).split('.')[0]
with open(pdb) as f1, open(f"ATOM_{pdb.split('.')[0].split('_')[0]}.pdb", 'w') as f2:
for line in f1:
if 'ATOM' in line:
f2.write(line)
with open(f'{raw}_ligand.pdb') as f1, open(f"HETATM_{pdb.split('.')[0].split('_')[0]}.pdb", 'w') as f2:
for line in f1:
if ('HETATM' in line) and not any(ion in line for ion in self.ions):
f2.write(line)
try:
self.get_HOH_pdb(pdb)
except:
with open(pdb) as f1, open(f"HOH_{pdb.split('.')[0].split('_')[0]}.pdb", 'w') as f2:
for line in f1:
if ('HETATM' in line) and any(ion in line for ion in self.ions):
f2.write(line)
return
def fragment_and_plif(self):
path = os.getcwd()
if not os.path.exists('results_plifs'):
os.mkdir(f'{path}/results_plifs')
raw=str(self.pdb).split('.')[0]
self.sdf_2_pdb(f'{raw}_ligand.sdf')
self.keep_relevant_hetatm(f'{raw}_protein.pdb')
fragment_mols = Chem.SDMolSupplier(str(f'{raw}_ligand.sdf'), removeHs=True, sanitize=False)
fragment_mols_alt = Chem.MolFromMol2File(f'{raw}_ligand.mol2', sanitize=True, removeHs=True)
content = open(f'{raw}_ligand.pdb').read()
hets=re.findall("^HETATM (.*)", content, re.M)
if len(hets)<5:
# Read in the file
with open(f'{raw}_ligand.pdb', 'r') as file :
filedata = file.read()
# Replace the target string
filedata = filedata.replace('ATOM ', 'HETATM')
# Write the file out again
with open(f'{raw}_ligand.pdb', 'w') as file:
file.write(filedata)
try:
fragment_mols = Chem.RemoveHs(fragment_mols[0])
output_df = self.split_interact_molecule(fragment_mols,raw)
except:
try:
fragment_mols = Chem.SDMolSupplier(str(f'{raw}_ligand.sdf'), removeHs=True, sanitize=False)
output_df = self.split_interact_molecule(fragment_mols[0],raw)
except:
try:
output_df = self.split_interact_molecule(fragment_mols_alt,raw)
except:
try:
fragment_mols = AllChem.MolFromPDBFile(f'{raw}_ligand.pdb')
output_df = self.split_interact_molecule(fragment_mols,raw)
except:
fragment_mols = AllChem.MolFromPDBFile(f'HETATM_{raw}.pdb')
output_df = self.split_interact_molecule(fragment_mols,raw)
os.chdir(f'{path}')
return output_df.groupby('SAMPLE_ID')['PROT_COORDS', 'LIG_COORDS','INTERACTION_TYPE','DIST','DESCRIPTORS_3D',
'FRAG_COORDS','MEMBERSHIPS','DESCRIPTORS_2D'].agg(list)
def kd_equalizer (value):
if 'mM' in value.split('=')[1]:
return float(value.split('m')[0].split('=')[1]) / 1000
elif 'uM' in value.split('=')[1]:
return float(value.split('u')[0].split('=')[1]) / 1000000
elif 'nM' in value.split('=')[1]:
return float(value.split('n')[0].split('=')[1]) / 1000000000
elif 'pM' in value.split('=')[1]:
return float(value.split('p')[0].split('=')[1]) / 1000000000000
elif 'fM' in value.split('=')[1]:
return float(value.split('f')[0].split('=')[1]) / 1000000000000000
def dask_plif_cnn_train(row_pdb, row_kd):
train_grids=None
train_label=[]
pdb_id = row_pdb
print('pdb_id', pdb_id)
os.chdir(f'/groups/cherkasvgrp/share/progressive_docking/hmslati/plif_cnn/general_refined_set/{pdb_id}')
raw=pdb_id
path = os.getcwd()
fileList = []
fileList.extend(glob.glob(f'{path}/{raw}_7*pdb'))
fileList.extend(glob.glob(f'{path}/{raw}_8*pdb'))
fileList.extend(glob.glob(f'{path}/{raw}_9*pdb'))
fileList.extend(glob.glob(f'{path}/*_{raw}*pdb'))
fileList.extend(glob.glob(f'{path}/*_{raw}*sdf'))
for filePath in fileList:
try:
os.remove(filePath)
except:
print("Error while deleting file : ", filePath)
global df_plifSpecs
df_plifSpecs = PLIF(PDB = f'{pdb_id}.pdb').fragment_and_plif()
if not len(df_plifSpecs) or df_plifSpecs['INTERACTION_TYPE'].apply(', '.join).unique()[0]=='metal':
return
train_label.extend([row_kd]*20)
single_pdb_frags = []
for idx, row in df_plifSpecs.iterrows():
plifs_prot={}
plifs_lig={}
plifs_frag={}
## do something with fragment_idx . i.e. open the pdb and do your shit with encoding\
for aa_atm_coord_list, lig_atm_coord_list, interaction, dist_list, des3D_list, fragCoor_list,mem_list, des2D in zip (
row['PROT_COORDS'],
row['LIG_COORDS'],
row['INTERACTION_TYPE'],
row['DIST'],
row['DESCRIPTORS_3D'],
row['FRAG_COORDS'],
row['MEMBERSHIPS'],
row['DESCRIPTORS_2D']):
# because sometimes salt bridges makes two concurrent connections so it is possible that we have one distance
# for two amino acids or ligand atoms! Encoding by atom is crazy fun
for dist, aa_atm_coord, lig_atm_coord, des3D, fragCoor in zip (dist_list, aa_atm_coord_list,
lig_atm_coord_list, des3D_list,
fragCoor_list):
plifs_prot[tuple(aa_atm_coord)]=[interaction]
plifs_lig[tuple(lig_atm_coord)]=[interaction]
plifs_frag[tuple(fragCoor)]=mem_list
pdb = next(pybel.readfile('pdb',os.path.join(path,'ATOM_' + pdb_id + '.pdb')))
ligand = next(pybel.readfile('pdb',os.path.join(path, f'HETATM_{pdb_id}'+'.pdb')))
single_pdb_frags.append((pdb,ligand,plifs_prot,plifs_lig,plifs_frag))
for idx, mols in enumerate(single_pdb_frags):
print(len(single_pdb_frags), idx)
# threads = multiprocessing.Pool(len(single_pdb_frags))
# threads.map(func, arg_list)
coords1, features1 = Feature.get_features(mols[0],mols[2],1)
coords2, features2 = Feature.get_features(mols[1],mols[3],0,mols[4])
# get the center point of protein
center=(np.max(coords2,axis=0)+np.min(coords2,axis=0))/2
coords=np.concatenate([coords1,coords2],axis = 0)
features=np.concatenate([features1,features2],axis = 0)
assert len(coords) == len(features)
# zero the coordinates
coords = coords-center
grid=Feature.grid(coords,features)
if train_grids is None:
train_grids = grid.to_sparse()
else:
train_grids = torch.cat((train_grids,grid.to_sparse()), 0)
print(train_grids.shape)
raw=pdb_id
path = os.getcwd()
fileList = []
fileList.extend(glob.glob(f'{path}/{raw}_7*pdb'))
fileList.extend(glob.glob(f'{path}/{raw}_8*pdb'))
fileList.extend(glob.glob(f'{path}/{raw}_9*pdb'))
fileList.extend(glob.glob(f'{path}/*_{raw}*pdb'))
fileList.extend(glob.glob(f'{path}/*_{raw}*sdf'))
for filePath in fileList:
try:
os.remove(filePath)
except:
print("Error while deleting file : ", filePath)
print("Memory utilised (bytes): ", sys.getsizeof(train_grids))
os.chdir('/groups/cherkasvgrp/share/progressive_docking/hmslati/plif_cnn/train')
for x in range(20):
with open(f"{pdb_id}_{x}_train_grids.pkl",'wb') as f:
pickle.dump(train_grids[x], f)
#Save the label data of training and test set.
with open(f"{pdb_id}_{x}_train_label.pkl",'wb') as f:
pickle.dump(train_label[x], f)
os.chdir(p_directory)
return pdb_id
def dask_plif_cnn_test(row_pdb, row_kd):
test_grids=None
test_label=[]
pdb_id = row_pdb
print('pdb_id', pdb_id)
os.chdir(f'/groups/cherkasvgrp/share/progressive_docking/hmslati/plif_cnn/general_refined_set/{pdb_id}')
raw=pdb_id
path = os.getcwd()
fileList = []
fileList.extend(glob.glob(f'{path}/{raw}_7*pdb'))
fileList.extend(glob.glob(f'{path}/{raw}_8*pdb'))
fileList.extend(glob.glob(f'{path}/{raw}_9*pdb'))
fileList.extend(glob.glob(f'{path}/*_{raw}*pdb'))
fileList.extend(glob.glob(f'{path}/*_{raw}*sdf'))
for filePath in fileList:
try:
os.remove(filePath)
except:
print("Error while deleting file : ", filePath)
df_plifSpecs = PLIF(PDB = f'{pdb_id}.pdb').fragment_and_plif()
if not len(df_plifSpecs) or df_plifSpecs['INTERACTION_TYPE'].apply(', '.join).unique()[0]=='metal':
return
test_label.extend([row_kd])
single_pdb_frags = []
for idx, row in df_plifSpecs.iterrows():
plifs_prot={}
plifs_lig={}
plifs_frag={}
## do something with fragment_idx . i.e. open the pdb and do your shit with encoding\
for aa_atm_coord_list, lig_atm_coord_list, interaction, dist_list, des3D_list, fragCoor_list,mem_list, des2D in zip (
row['PROT_COORDS'],
row['LIG_COORDS'],
row['INTERACTION_TYPE'],
row['DIST'],
row['DESCRIPTORS_3D'],
row['FRAG_COORDS'],
row['MEMBERSHIPS'],
row['DESCRIPTORS_2D']):
# because sometimes salt bridges makes two concurrent connections so it is possible that we have one distance
# for two amino acids or ligand atoms! Encoding by atom is crazy fun
for dist, aa_atm_coord, lig_atm_coord, des3D, fragCoor in zip (dist_list, aa_atm_coord_list,
lig_atm_coord_list, des3D_list,
fragCoor_list):
plifs_prot[tuple(aa_atm_coord)]=[interaction]
plifs_lig[tuple(lig_atm_coord)]=[interaction]
plifs_frag[tuple(fragCoor)]=mem_list
pdb = next(pybel.readfile('pdb',os.path.join(path,'ATOM_' + pdb_id + '.pdb')))
ligand = next(pybel.readfile('pdb',os.path.join(path, f'HETATM_{pdb_id}'+'.pdb')))
single_pdb_frags.append((pdb,ligand,plifs_prot,plifs_lig,plifs_frag))
for idx, mols in enumerate(single_pdb_frags):
print(len(single_pdb_frags), idx)
# threads = multiprocessing.Pool(len(single_pdb_frags))
# threads.map(func, arg_list)
coords1, features1 = Feature.get_features(mols[0],mols[2],1)
coords2, features2 = Feature.get_features(mols[1],mols[3],0,mols[4])