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import sys
import numpy as np
import shutil
import glob
import pymesh
import Bio.PDB
from Bio.PDB import *
from rdkit import Chem
import warnings
warnings.filterwarnings("ignore")
from IPython.utils import io
from sklearn.neighbors import KDTree
from scipy.spatial import distance
from default_config.masif_opts import masif_opts
from compute_normal import compute_normal
from computeAPBS import computeAPBS
from computeCharges import computeCharges, assignChargesToNewMesh
from computeHydrophobicity import computeHydrophobicity
from computeMSMS import computeMSMS
from fixmesh import fix_mesh
from save_ply import save_ply
from mol2graph import *
def compute_inp_surface(target_filename, ligand_filename,out_dir = None, dist_threshold=10):
try:
sufix = '_'+str(dist_threshold)+'A.pdb'
# out_filename = os.path.splitext(target_filename)[0]
if out_dir is not None:
out_filename = os.path.join(out_dir,ligand_filename.split('/')[-2])
os.makedirs(out_filename,exist_ok=True)
sufix = '/' + os.path.splitext(target_filename)[0].split('/')[-1] + '_'+str(dist_threshold)+'A.pdb'
else:
out_filename = os.path.splitext(ligand_filename)[0]
if os.path.exists(out_filename+f"/{sufix.split('.pdb')[0]}.ply"):
print('have done skip!')
return 0
input_filename = os.path.splitext(target_filename)[0]
# Get atom coordinates
# try:
# mol = Chem.MolFromMol2File(ligand_filename, sanitize=False, cleanupSubstructures=False)
# except:
# print('mol2 faild try sdf')
if ligand_filename.endswith('.sdf'):
mol = Chem.SDMolSupplier(ligand_filename, sanitize=False)[0]
elif ligand_filename.endswith('.pdb'):
mol = Chem.MolFromPDBFile(ligand_filename, sanitize=False)
g = mol_to_nx(mol)
atomCoords = np.array([g.nodes[i]['pos'].tolist() for i in g.nodes])
# Read protein and select aminino acids in the binding pocket
parser = Bio.PDB.PDBParser(QUIET=True) # QUIET=True avoids comments on errors in the pdb.
structures = parser.get_structure('target', input_filename+'.pdb')
structure = structures[0] # 'structures' may contain several proteins in this case only one.
atoms = Bio.PDB.Selection.unfold_entities(structure, 'A')
ns = Bio.PDB.NeighborSearch(atoms)
close_residues= []
for a in atomCoords:
close_residues.extend(ns.search(a, dist_threshold, level='R'))
close_residues = Bio.PDB.Selection.uniqueify(close_residues)
class SelectNeighbors(Select):
def accept_residue(self, residue):
if residue in close_residues:
if all(a in [i.get_name() for i in residue.get_unpacked_list()] for a in ['N', 'CA', 'C', 'O']) or residue.resname=='HOH':
return True
else:
return False
else:
return False
pdbio = PDBIO()
pdbio.set_structure(structure)
pdbio.save(out_filename+sufix, SelectNeighbors())
# Identify closes atom to the ligand
structures = parser.get_structure('target', out_filename+sufix)
structure = structures[0] # 'structures' may contain several proteins in this case only one.
atoms = Bio.PDB.Selection.unfold_entities(structure, 'A')
#dist = [distance.euclidean(atomCoords.mean(axis=0), a.get_coord()) for a in atoms]
#atom_idx = np.argmin(dist)
#dist = [[distance.euclidean(ac, a.get_coord()) for ac in atomCoords] for a in atoms]
#atom_idx = np.argsort(np.min(dist, axis=1))[0]
# Compute MSMS of surface w/hydrogens,
try:
dist = [distance.euclidean(atomCoords.mean(axis=0), a.get_coord()) for a in atoms]
atom_idx = np.argmin(dist)
vertices1, faces1, normals1, names1, areas1 = computeMSMS(out_filename+sufix,\
protonate=True, one_cavity=atom_idx)
# Find the distance between every vertex in binding site surface and each atom in the ligand.
kdt = KDTree(atomCoords)
d, r = kdt.query(vertices1)
assert(len(d) == len(vertices1))
iface_v = np.where(d <= dist_threshold-5)[0]
faces_to_keep = [idx for idx, face in enumerate(faces1) if all(v in iface_v for v in face)]
# Compute "charged" vertices
if masif_opts['use_hbond']:
vertex_hbond = computeCharges(input_filename, vertices1, names1)
# For each surface residue, assign the hydrophobicity of its amino acid.
if masif_opts['use_hphob']:
vertex_hphobicity = computeHydrophobicity(names1)
# If protonate = false, recompute MSMS of surface, but without hydrogens (set radius of hydrogens to 0).
vertices2 = vertices1
faces2 = faces1
# Fix the mesh.
mesh = pymesh.form_mesh(vertices2, faces2)
mesh = pymesh.submesh(mesh, faces_to_keep, 0)
with io.capture_output() as captured:
regular_mesh = fix_mesh(mesh, masif_opts['mesh_res'])
except:
try:
dist = [[distance.euclidean(ac, a.get_coord()) for ac in atomCoords] for a in atoms]
atom_idx = np.argsort(np.min(dist, axis=1))[0]
vertices1, faces1, normals1, names1, areas1 = computeMSMS(out_filename+sufix,\
protonate=True, one_cavity=atom_idx)
# Find the distance between every vertex in binding site surface and each atom in the ligand.
kdt = KDTree(atomCoords)
d, r = kdt.query(vertices1)
assert(len(d) == len(vertices1))
iface_v = np.where(d <= dist_threshold-5)[0]
faces_to_keep = [idx for idx, face in enumerate(faces1) if all(v in iface_v for v in face)]
# Compute "charged" vertices
if masif_opts['use_hbond']:
vertex_hbond = computeCharges(input_filename, vertices1, names1)
# For each surface residue, assign the hydrophobicity of its amino acid.
if masif_opts['use_hphob']:
vertex_hphobicity = computeHydrophobicity(names1)
# If protonate = false, recompute MSMS of surface, but without hydrogens (set radius of hydrogens to 0).
vertices2 = vertices1
faces2 = faces1
# Fix the mesh.
mesh = pymesh.form_mesh(vertices2, faces2)
mesh = pymesh.submesh(mesh, faces_to_keep, 0)
with io.capture_output() as captured:
regular_mesh = fix_mesh(mesh, masif_opts['mesh_res'])
except:
vertices1, faces1, normals1, names1, areas1 = computeMSMS(out_filename+sufix,\
protonate=True, one_cavity=None)
# Find the distance between every vertex in binding site surface and each atom in the ligand.
kdt = KDTree(atomCoords)
d, r = kdt.query(vertices1)
assert(len(d) == len(vertices1))
iface_v = np.where(d <= dist_threshold-5)[0]
faces_to_keep = [idx for idx, face in enumerate(faces1) if all(v in iface_v for v in face)]
# Compute "charged" vertices
if masif_opts['use_hbond']:
vertex_hbond = computeCharges(input_filename, vertices1, names1)
# For each surface residue, assign the hydrophobicity of its amino acid.
if masif_opts['use_hphob']:
vertex_hphobicity = computeHydrophobicity(names1)
# If protonate = false, recompute MSMS of surface, but without hydrogens (set radius of hydrogens to 0).
vertices2 = vertices1
faces2 = faces1
# Fix the mesh.
mesh = pymesh.form_mesh(vertices2, faces2)
mesh = pymesh.submesh(mesh, faces_to_keep, 0)
with io.capture_output() as captured:
regular_mesh = fix_mesh(mesh, masif_opts['mesh_res'])
# Compute the normals
vertex_normal = compute_normal(regular_mesh.vertices, regular_mesh.faces)
# Assign charges on new vertices based on charges of old vertices (nearest
# neighbor)
if masif_opts['use_hbond']:
vertex_hbond = assignChargesToNewMesh(regular_mesh.vertices, vertices1,\
vertex_hbond, masif_opts)
if masif_opts['use_hphob']:
vertex_hphobicity = assignChargesToNewMesh(regular_mesh.vertices, vertices1,\
vertex_hphobicity, masif_opts)
if masif_opts['use_apbs']:
vertex_charges = computeAPBS(regular_mesh.vertices, out_filename+sufix, out_filename+"_temp")
# Compute the principal curvature components for the shape index.
regular_mesh.add_attribute("vertex_mean_curvature")
H = regular_mesh.get_attribute("vertex_mean_curvature")
regular_mesh.add_attribute("vertex_gaussian_curvature")
K = regular_mesh.get_attribute("vertex_gaussian_curvature")
elem = np.square(H) - K
# In some cases this equation is less than zero, likely due to the method that computes the mean and gaussian curvature.
# set to an epsilon.
elem[elem<0] = 1e-8
k1 = H + np.sqrt(elem)
k2 = H - np.sqrt(elem)
# Compute the shape index
si = (k1+k2)/(k1-k2)
si = np.arctan(si)*(2/np.pi)
# Convert to ply and save.
save_ply(out_filename+f"/{sufix.split('.pdb')[0]}.ply", regular_mesh.vertices,\
regular_mesh.faces, normals=vertex_normal, charges=vertex_charges,\
normalize_charges=True, hbond=vertex_hbond, hphob=vertex_hphobicity,\
si=si)
return 0
except:
return target_filename
if __name__ == "__main__":
from joblib import delayed,Parallel
# arguments
from argparse import ArgumentParser, Namespace, FileType
parser = ArgumentParser()
parser.add_argument('--data_dir', type=str, default='~/SurfDock/model/data/test_samples', help='')
parser.add_argument('--out_dir', type=str, default='~/SurfDock/model/data/test_samples_8A_surface', help='')
parser.add_argument('--n_jobs',type=int, default=1, help='Number of parallel jobs (-1 for all CPUs)')
args = parser.parse_args()
os.makedirs(args.out_dir,exist_ok=True)
sys.path.append(args.out_dir)
from tqdm import tqdm
args_list = []
for protein in tqdm(os.listdir(args.data_dir)):
if os.path.exists(os.path.join(args.out_dir,protein,f'{protein}_protein_processed_obabel_reduce_obabel.pdb')):
target_filename = os.path.join(args.out_dir,protein,f'{protein}_protein_processed_obabel_reduce_obabel.pdb')
elif os.path.exists(os.path.join(args.data_dir,protein,f'{protein}_protein_processed.pdb')):
target_filename = os.path.join(args.data_dir,protein,f'{protein}_protein_processed.pdb')
print(f'{protein} use {target_filename}; Please check this protein file was processed by openbabel reduce! in protein_process')
else:
print(f'{protein} not exists , Please check file name or path')
continue
ligand_filename = os.path.join(args.data_dir,protein,f'{protein}_ligand.sdf')
if not os.path.exists(ligand_filename):
ligand_filename = os.path.join(args.data_dir,protein,f'{protein}_ligand.mol2')
args_list.append((target_filename,ligand_filename))
print(f'number {len(args_list)} need to processed.....')
results = Parallel(n_jobs = args.n_jobs,backend = 'multiprocessing')(delayed(compute_inp_surface)(target_filename, ligand_filename,args.out_dir, dist_threshold=8) for (target_filename, ligand_filename) in tqdm(args_list))
# print(results)
# Find all files in args.out_dir that end with _temp
files = glob.glob(os.path.join(args.out_dir, '*_temp*')) + glob.glob(os.path.join(args.out_dir, '*msms*'))
# Delete all found files
for f in files:
os.remove(f)
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