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#
# * This library is free software; you can redistribute it and/or
# * modify it under the terms of the GNU Lesser General Public
# * License as published by the Free Software Foundation; either
# * version 2.1 of the License, or (at your option) any later version.
# *
# * This library is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# * Lesser General Public License for more details.
#
#propka3.0, revision 182 2011-08-09
#-------------------------------------------------------------------------------------------------------
#-- --
#-- PROPKA: A PROTEIN PKA PREDICTOR --
#-- --
#-- VERSION 3.0, 01/01/2011, COPENHAGEN --
#-- BY MATS H.M. OLSSON AND CHRESTEN R. SONDERGARD --
#-- --
#-------------------------------------------------------------------------------------------------------
#
#
#-------------------------------------------------------------------------------------------------------
# References:
#
# Very Fast Empirical Prediction and Rationalization of Protein pKa Values
# Hui Li, Andrew D. Robertson and Jan H. Jensen
# PROTEINS: Structure, Function, and Bioinformatics 61:704-721 (2005)
#
# Very Fast Prediction and Rationalization of pKa Values for Protein-Ligand Complexes
# Delphine C. Bas, David M. Rogers and Jan H. Jensen
# PROTEINS: Structure, Function, and Bioinformatics 73:765-783 (2008)
#
# PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa predictions
# Mats H.M. Olsson, Chresten R. Sondergard, Michal Rostkowski, and Jan H. Jensen
# Journal of Chemical Theory and Computation, 7, 525-537 (2011)
#-------------------------------------------------------------------------------------------------------
import pickle,sys,os,math
from .lib import pka_print
class bondmaker:
def __init__(self):
self.SS_dist = 2.5;
self.H_dist = 1.5;
self.default_dist = 2.0;
self.SS_dist_squared = self.SS_dist * self.SS_dist
self.H_dist_squared = self.H_dist * self.H_dist
self.default_dist_squared = self.default_dist * self.default_dist
self.data_file_name = "%s/%s" % (os.path.dirname(__file__), "protein_bonds.dat")
data = open(self.data_file_name,'rb')
self.protein_bonds = pickle.load(data)
data.close()
self.intra_residue_backbone_bonds = {'N': ['CA'],
'CA':['N','C'],
'C': ['CA','O'],
'O': ['C']}
self.number_of_pi_electrons_in_bonds_in_backbone = {'C':1,
'O':1}
self.number_of_pi_electrons_in_conjugate_bonds_in_backbone = {'N':1}
self.number_of_pi_electrons_in_bonds_in_sidechains = {'ARG-CZ' :1,
'ARG-NH1':1,
'ASN-OD1':1,
'ASN-CG' :1,
'ASP-OD1':1,
'ASP-CG' :1,
'GLU-OE1':1,
'GLU-CD' :1,
'GLN-OE1':1,
'GLN-CD' :1,
'HIS-CG' :1,
'HIS-CD2':1,
'HIS-ND1':1,
'HIS-CE1':1,
'PHE-CG' :1,
'PHE-CD1':1,
'PHE-CE1':1,
'PHE-CZ' :1,
'PHE-CE2':1,
'PHE-CD2':1,
'TRP-CG' :1,
'TRP-CD1':1,
'TRP-CE2':1,
'TRP-CD2':1,
'TRP-CE3':1,
'TRP-CZ3':1,
'TRP-CH2':1,
'TRP-CZ2':1,
'TYR-CG' :1,
'TYR-CD1':1,
'TYR-CE1':1,
'TYR-CZ' :1,
'TYR-CE2':1,
'TYR-CD2':1}
self.number_of_pi_electrons_in_conjugate_bonds_in_sidechains = {'ARG-NE' :1,
'ARG-NH2':1,
'ASN-ND2':1,
'GLN-NE2':1,
'HIS-NE2':1,
'TRP-NE1':1}
self.number_of_pi_electrons_in_bonds_ligands = {'C.ar':1,
'N.pl3':0,
'C.2':1,
'O.2':1,
'O.co2':1,
'N.ar':1,
'C.1':2,
'N.1':2}
self.number_of_pi_electrons_in_conjugate_bonds_in_ligands = {'N.am':1,'N.pl3':1}
self.backbone_atoms = list(self.intra_residue_backbone_bonds.keys())
self.terminal_oxygen_names = ['OXT','O\'\'']
#pka_print((self.protein_bonds))
return
def find_bonds_for_protein(self, protein):
""" Finds bonds proteins based on the way atoms
normally bond in proteins"""
#pka_print('++++ Side chains ++++')
# side chains
for chain in protein.chains:
for residue in chain.residues:
if residue.type == "amino-acid":
self.find_bonds_for_side_chain(residue.atoms)
#pka_print('++++ Backbones ++++')
# backbone
last_residues = []
for chain in protein.chains:
for i in range(1,len(chain.residues)):
if chain.residues[i-1].resName.replace(' ','') not in ['N+','C-']:
if chain.residues[i].resName.replace(' ','') not in ['N+','C-']:
self.connect_backbone(chain.residues[i-1], chain.residues[i])
last_residues.append(chain.residues[i])
#pka_print('++++ terminal oxygen ++++')
# terminal OXT
for last_residue in last_residues:
self.find_bonds_for_terminal_oxygen(last_residue)
#pka_print('++++ cysteines ++++')
# Cysteines
for chain in protein.chains:
for i in range(0,len(chain.residues)):
if chain.residues[i].resName == 'CYS':
for j in range(0,len(chain.residues)):
if chain.residues[j].resName == 'CYS' and j != i:
self.check_for_cysteine_bonds(chain.residues[i],
chain.residues[j])
return
def check_for_cysteine_bonds(self, cys1, cys2):
for atom1 in cys1.atoms:
if atom1.name == 'SG':
for atom2 in cys2.atoms:
if atom2.name == 'SG':
if self.squared_distance(atom1,atom2) < self.SS_dist_squared:
self.make_bond(atom1, atom2)
return
def find_bonds_for_terminal_oxygen(self, residue):
for atom1 in residue.atoms:
if atom1.name in self.terminal_oxygen_names:
for atom2 in residue.atoms:
if atom2.name == 'C':
self.make_bond(atom1, atom2)
return
def connect_backbone(self, residue1, residue2):
""" Sets up bonds in the backbone """
# residue 1
self.find_bonds_for_residue_backbone(residue1)
# residue 2
self.find_bonds_for_residue_backbone(residue2)
# inter-residue bond
for atom1 in residue1.atoms:
if atom1.name == 'C':
for atom2 in residue2.atoms:
if atom2.name == 'N':
if self.squared_distance(atom1,atom2) < self.default_dist_squared:
self.make_bond(atom1, atom2)
return
def find_bonds_for_residue_backbone(self, residue):
for atom1 in residue.atoms:
if atom1.name in list(self.number_of_pi_electrons_in_bonds_in_backbone.keys()):
atom1.number_of_pi_electrons_in_double_and_triple_bonds = self.number_of_pi_electrons_in_bonds_in_backbone[atom1.name]
if atom1.name in list(self.number_of_pi_electrons_in_conjugate_bonds_in_backbone.keys()) and len(atom1.bonded_atoms)>1: # last part to avoid including N-term
atom1.number_of_pi_electrons_in_conjugate_double_and_triple_bonds = self.number_of_pi_electrons_in_conjugate_bonds_in_backbone[atom1.name]
if atom1.name in self.backbone_atoms:
for atom2 in residue.atoms:
if atom2.name in self.intra_residue_backbone_bonds[atom1.name]:
self.make_bond(atom1, atom2)
return
def find_bonds_for_side_chain(self, atoms):
""" Finds bonds for a side chain """
for atom1 in atoms:
key = '%s-%s'%(atom1.resName,atom1.name)
if key in list(self.number_of_pi_electrons_in_bonds_in_sidechains.keys()):
atom1.number_of_pi_electrons_in_double_and_triple_bonds = self.number_of_pi_electrons_in_bonds_in_sidechains[key]
if key in list(self.number_of_pi_electrons_in_conjugate_bonds_in_sidechains.keys()):
atom1.number_of_pi_electrons_in_conjugate_double_and_triple_bonds = self.number_of_pi_electrons_in_conjugate_bonds_in_sidechains[key]
if not atom1.name in self.backbone_atoms:
if not atom1.name in self.terminal_oxygen_names:
for atom2 in atoms:
if atom2.name in self.protein_bonds[atom1.resName][atom1.name]:
self.make_bond(atom1,atom2)
return
def find_bonds_for_ligand(self, ligand):
""" Finds bonds for all atoms in the molecule """
# identify bonding atoms
self.find_bonds_for_atoms(ligand.atoms)
# apply table information on pi-electrons
for atom in ligand.atoms:
if atom.name in list(self.number_of_pi_electrons_in_bonds_ligands.keys()):
atom.number_of_pi_electrons_in_double_and_triple_bonds = self.number_of_pi_electrons_in_bonds_ligands[atom.name]
if atom.name in list(self.number_of_pi_electrons_in_conjugate_bonds_in_ligands.keys()):
atom.number_of_pi_electrons_in_conjugate_double_and_triple_bonds = self.number_of_pi_electrons_in_conjugate_bonds_in_ligands[atom.name]
# just in case any protein residues are included
key = '%s-%s'%(atom.resName,atom.name)
if key in list(self.number_of_pi_electrons_in_bonds_in_sidechains.keys()):
atom.number_of_pi_electrons_in_double_and_triple_bonds = self.number_of_pi_electrons_in_bonds_in_sidechains[key]
if key in list(self.number_of_pi_electrons_in_conjugate_bonds_in_sidechains.keys()):
atom.number_of_pi_electrons_in_conjugate_double_and_triple_bonds = self.number_of_pi_electrons_in_conjugate_bonds_in_sidechains[key]
if atom.name in list(self.number_of_pi_electrons_in_bonds_in_backbone.keys()):
atom.number_of_pi_electrons_in_double_and_triple_bonds = self.number_of_pi_electrons_in_bonds_in_backbone[atom.name]
if atom.name in list(self.number_of_pi_electrons_in_conjugate_bonds_in_backbone.keys()) and len(atom.bonded_atoms)>1: # last part to avoid including N-term
atom.number_of_pi_electrons_in_conjugate_double_and_triple_bonds = self.number_of_pi_electrons_in_conjugate_bonds_in_backbone[atom.name]
return
def find_bonds_for_protein_by_distance(self, molecule):
""" Finds bonds for all atoms in the molecule """
#self.find_bonds_for_protein(molecule)
atoms = []
for chain in molecule.chains:
for residue in chain.residues:
if residue.resName.replace(' ','') not in ['N+','C-']:
for atom in residue.atoms:
atoms.append(atom)
self.find_bonds_for_atoms_using_boxes(atoms) #####
#self.find_bonds_for_atoms(atoms) #####
return atoms
def find_bonds_for_atoms(self, atoms):
""" Finds all bonds for a list of atoms"""
#pka_print('Found %d atoms' %(len(atoms)))
for i in range(len(atoms)):
for j in range(i+1,len(atoms)):
sq_dist = self.squared_distance(atoms[i], atoms[j])
if sq_dist < self.SS_dist_squared:
if sq_dist < self.default_dist_squared:
self.make_bond(atoms[i],atoms[j])
elif atoms[i].get_element() == 'S' and \
atoms[j].get_element() == 'S':
# pka_print('Di-sulphide bond',atoms[i],atoms[j])
self.make_bond(atoms[i],atoms[j])
return
def find_bonds_for_atoms_using_boxes(self, atoms):
""" Finds all bonds for a list of atoms"""
pka_print('Found %d atoms' %(len(atoms)))
box_size = 2.5
# find min and max coordinates
xmin = 1e6; xmax = -1e6
ymin = 1e6; ymax = -1e6
zmin = 1e6; zmax = -1e6
for atom in atoms:
if atom.x > xmax:
xmax = atom.x
if atom.y > ymax:
ymax = atom.y
if atom.z > zmax:
zmax = atom.z
if atom.x < xmin:
xmin = atom.x
if atom.y < ymin:
ymin = atom.y
if atom.z < zmin:
zmin = atom.z
xlen = xmax-xmin
ylen = ymax-ymin
zlen = zmax-zmin
pka_print('x range: [%6.2f;%6.2f] %6.2f'%(xmin,xmax,xlen))
pka_print('y range: [%6.2f;%6.2f] %6.2f'%(ymin,ymax,ylen))
pka_print('z range: [%6.2f;%6.2f] %6.2f'%(zmin,zmax,zlen))
# how many boxes do we need in each dimension?
self.no_box_x = math.ceil(xlen/box_size)
self.no_box_y = math.ceil(ylen/box_size)
self.no_box_z = math.ceil(zlen/box_size)
pka_print('No. box x: %6.2f'%self.no_box_x)
pka_print('No. box y: %6.2f'%self.no_box_y)
pka_print('No. box z: %6.2f'%self.no_box_z)
# initialize boxes
self.boxes = {}
for x in range(self.no_box_x):
for y in range(self.no_box_y):
for z in range(self.no_box_z):
self.boxes[self.box_key(x,y,z)] = []
# put atoms into boxes
for atom in atoms:
x = math.floor((atom.x-xmin)/box_size)
y = math.floor((atom.y-ymin)/box_size)
z = math.floor((atom.z-zmin)/box_size)
self.put_atom_in_box(x,y,z,atom)
# assign bonds
keys = list(self.boxes.keys())
for key in keys:
self.find_bonds_for_atoms(self.boxes[key])
return
def put_atom_in_box(self,x,y,z,atom):
# atom in the x,y,z box and the up to 26 neighboring boxes
for bx in [x-1,x,x+1]:
for by in [y-1,y,y+1]:
for bz in [z-1,z,z+1]:
key = self.box_key(bx,by,bz)
if key in list(self.boxes.keys()):
self.boxes[key].append(atom)
#pka_print(atom,'->',key,':',len(self.boxes[key]))
return
def box_key(self, x, y, z):
return '%d-%d-%d'%(x,y,z)
def squared_distance(self, atom_a, atom_b):
dx = atom_a.x - atom_b.x
dy = atom_a.y - atom_b.y
dz = atom_a.z - atom_b.z
return dx*dx + dy*dy + dz*dz
def has_bond(self, atom1, atom2):
if atom1 in atom2.bonded_atoms or atom2 in atom1.bonded_atoms:
return True
return False
def make_bond(self, atom1, atom2):
""" Makes a bond between atom1 and atom2 """
if atom1 == atom2:
return
if not atom1 in atom2.bonded_atoms:
#pka_print(atom1,' - ',atom2)
atom2.bonded_atoms.append(atom1)
if not atom2 in atom1.bonded_atoms:
atom1.bonded_atoms.append(atom2)
return
def generate_protein_bond_dictionary(self, atoms):
for atom in atoms:
for bonded_atom in atom.bonded_atoms:
resi_i = atom.resName
name_i = atom.name
resi_j = bonded_atom.resName
name_j = bonded_atom.name
if not name_i in self.backbone_atoms or\
not name_j in self.backbone_atoms:
if not name_i in self.terminal_oxygen_names and\
not name_j in self.terminal_oxygen_names:
if not resi_i in list(self.protein_bonds.keys()):
self.protein_bonds[resi_i] = {}
if not name_i in self.protein_bonds[resi_i]:
self.protein_bonds[resi_i][name_i] = []
if not name_j in self.protein_bonds[resi_i][name_i]:
self.protein_bonds[resi_i][name_i].append(name_j)
if not resi_j in list(self.protein_bonds.keys()):
self.protein_bonds[resi_j] = {}
if not name_j in self.protein_bonds[resi_j]:
self.protein_bonds[resi_j][name_j] = []
if not name_i in self.protein_bonds[resi_j][name_j]:
self.protein_bonds[resi_j][name_j].append(name_i)
return
if __name__ == '__main__':
# If called directly, set up protein bond dictionary
import protein, pdb, sys,os
arguments = sys.argv
if len(arguments) != 2:
pka_print('Usage: bonds.py <pdb_file>')
sys.exit(0)
filename = arguments[1]
if not os.path.isfile(filename):
pka_print('Error: Could not find \"%s\"'%filename)
sys.exit(1)
pdblist = pdb.readPDB(filename)
my_protein = protein.Protein(pdblist,'test.pdb')
for chain in my_protein.chains:
for residue in chain.residues:
residue.atoms = [atom for atom in residue.atoms if atom.get_element() != 'H']
b = bondmaker()
#b.protein_bonds = {}
atoms = b.find_bonds_for_protein_by_distance(my_protein)
# b.generate_protein_bond_dictionary(atoms)
#file = open(b.data_file_name,'wb')
#pickle.dump(b.protein_bonds, file)
#file.close()
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