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ysera2.py
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ysera2.py
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from Bio.PDB import PDBParser, Selection, NeighborSearch
from Bio.PDB.vectors import calc_angle
import numpy as np
import pandas as pd
import time
import mdtraj as md
from aromatics import AromaticsFormat
class Nodes:
def __init__(self, name_=None, file_=False):
self.name = name_
self.file = file_
self.parser = PDBParser(PERMISSIVE=1)
self.structure = self.parser.get_structure(name_, file_)
self.ns = NeighborSearch(list(self.structure.get_atoms()))
# mdtraj
self.pdb = md.load_pdb(file_)
self.dssp_md = md.compute_dssp(self.pdb, simplified=False)
self.model = self.structure[0]
self.all_dssps = []
self.nodes_id, self.chains, self.positions, self.residues = [], [], [], []
self.degrees = []
self.cut_dist = 8.0 # Defining a distance cutoff limit (chosen based on the literature).
# B-Factor, coords and filenames
self.bfactors, self.coords, self.pdb_filename = [], [], []
self.rapdfs = []
self.models = []
def get_node_degrees(self):
edges = Edges(self.name, self.file, multiple=True)
edges.Bonds()
if edges.multiple:
edges.multiple_mode()
# getting the number of ligands in a residue
for node in self.nodes_id:
degree = 0
degree += edges.nodes_id1.count(node)
degree += edges.nodes_id2.count(node)
self.degrees.append(degree)
def search_nodes(self):
for model in self.structure:
for chain in model:
for residue in chain:
if str(residue.resname) != 'HOH': # ignore solvent
# Node_ID, Chain, Position and Residue
self.nodes_id.append(f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}")
self.chains.append(str(chain.id))
self.positions.append(residue.id[1])
if str(residue.resname) == '032':
# if the residue in 032, then it will not have a Bfactor-CA or coordinates
self.bfactors.append(' ')
self.coords.append(np.array([' ', ' ', ' ']))
self.residues.append(str(residue.resname))
# Bfactor_CA
b_factor = 0
count = 0
for atom in residue:
if (atom.get_name() == 'CA'):
b_factor += atom.get_bfactor()
count += 1
coords = atom.get_coord()
self.coords.append(coords)
if (count != 0):
bf_average = b_factor / count
self.bfactors.append(f"{bf_average:.3f}")
# pdb filenames
self.pdb_filename.append(f"input_file.cif#{str(residue.id[1])}.{str(chain.id)}")
self.models.append(model.id + 1)
# Rapdf
sum_of_dis = 0
rapdf_count = 0
for residue_2 in Selection.unfold_entities(model, 'R'):
if 'CA' in residue and 'CA' in residue_2 and residue_2.id[1] != residue.id[1]:
sum_of_dis += np.linalg.norm(residue["CA"].coord - residue_2["CA"].coord)
rapdf_count += 1
if rapdf_count != 0:
rapdf = sum_of_dis / rapdf_count
self.rapdfs.append(rapdf)
else:
self.rapdfs.append(0.0)
# DSSP
for i in range(len(self.dssp_md[0])):
if i > 0:
if self.dssp_md[0][i] == 'NA':
pass
else:
self.all_dssps.append(self.dssp_md[0][i] if self.dssp_md[0][i] != 'C' else ' ')
else:
self.all_dssps.append(self.dssp_md[0][i] if self.dssp_md[0][i] not in ['C', 'NA'] else ' ')
def print_output(self):
self.search_nodes()
for n in range(len(self.nodes_id)):
try:
print(
f"{self.nodes_id[n]}\t{self.chains[n]}\t\t{self.positions[n]}\t\t{self.residues[n]}\t{self.all_dssps[n]}\t" +
f"{self.degrees[n]}\t{self.bfactors[n]:.3f}\t{self.coords[n][0]:.3f}\t{self.coords[n][1]:.3f}\t{self.coords[n][2]:.3f}\t" +
f"{self.pdb_filename[n]}\t{self.rapdfs[n]}"
)
except Exception as e:
print(
f"{self.nodes_id[n]}\t{self.chains[n]}\t\t{self.positions[n]}\t\t{self.residues[n]}\t{self.all_dssps[n]}\t" +
f"{self.degrees[n]}\t{self.bfactors[n]}\t{self.coords[n][0]}\t{self.coords[n][1]}\t{self.coords[n][2]}\t" +
f"{self.pdb_filename[n]}\t{self.rapdfs[n]}"
)
def to_file(self):
#self.search_nodes()
colunas = ["NodeId", "Chain", "Position", "Residue", "Dssp", "Degree", "Bfactor_CA", "x", "y", "z",
"pdbFileName", "Model"]
x, y, z = [], [], []
for coord in self.coords:
if coord[0] != ' ':
x.append(f"{coord[0]:.3f}")
y.append(f"{coord[1]:.3f}")
z.append(f"{coord[2]:.3f}")
else:
x.append(coord[0])
y.append(coord[1])
z.append(coord[2])
data = pd.DataFrame(list(zip(self.nodes_id, self.chains, self.positions, self.residues,
self.all_dssps, self.degrees, self.bfactors, x, y, z, self.pdb_filename,
self.models
)), columns=colunas)
data.to_csv(f'./{self.name}_nodes.txt', sep='\t', index=False)
class Edges(Nodes):
def __init__(self, name, file_pdb, multiple=True):
af = AromaticsFormat(file_pdb)
self.aromatic_array, self.aromatic_normals, self.invalids = af.get_data()
Nodes.__init__(self, name_=name, file_=file_pdb)
self.lighbdonor = {'ARG': ['NE', 'NH1', 'NH2'],
'ASN': ['ND2'],
'HIS': ['NE2', 'ND1'],
'SER': ['OG'],
'TYR': ['OH'],
'CYS': ['SG'],
'THR': ['OG1'],
'GLN': ['NE2'],
'LYS': ['NZ'],
'TRP': ['NE1']
}
self.lighbac = {'ASN': ['OD1'],
'GLN': ['OE1'],
'MET': ['SD'],
'ASP': ['OD1', 'OD2'],
'GLU': ['OE1', 'OE2'],
'SER': ['OG'],
'THR': ['OG1'],
'HIS': ['ND1'],
'TYR': ['OH']
}
self.ligvdw = ['C', 'CB', 'CG1', 'CG2', 'CD1', 'CD2', 'CE']
self.ligpipi = ['HIS', 'TYR', 'TRP', 'PHE']
self.nodes_id1, self.nodes_id2, self.bonds = [], [], []
self.distances, self.donors, self.angles = [], [], []
self.atom1, self.atom2 = [], []
self.bonds_check, self.energies, self.orientation = [], [], []
self.analyzed_pairs = set()
self.multiple = multiple
self.ligands = {'hb': 0, 'vdw': 0, 'ionic': 0, 'sbond': 0, 'pi_stacking': 0, 'pi_cation': 0}
self.exclusions = []
self.positives, self.cations = [], []
def Iac(self):
# The new version doesn't calculate these bonds anymore.
lig_032 = []
for residue in self.structure.get_residues():
if str(residue.resname) == "032":
lig_032.append(residue)
for residue in lig_032:
for atom in residue:
for neighbor_pair in self.ns.search(atom.coord, 6.5, level='R'):
for atom2 in neighbor_pair:
if atom2.get_name() == 'CA':
distance = np.linalg.norm(atom.coord - atom2.coord)
# Checking if the neighboring atom is from another residue.
if neighbor_pair != residue:
print(residue.resname, neighbor_pair.resname, neighbor_pair.id[1], distance)
def add_bond(self, config, ligand):
"""
config's params: chain, residue, neighbor residue,
bond, distance, angle, energie,
atom1, atom2, donor.
ligand: hb, vdw, ionic...
"""
self.nodes_id1.append(f"{config[0].id}:{str(config[1].id[1])}:_:{str(config[1].resname)}")
self.nodes_id2.append(f"{config[2].get_parent().id}:{str(config[2].id[1])}:_:{str(config[2].resname)}")
self.bonds.append(config[3])
self.distances.append(config[4])
self.angles.append(config[5])
if ligand == 'hb':
if float(config[4]) <= 1.5:
self.energies.append(config[6][0])
elif float(config[4]) >= 2.2:
self.energies.append(config[6][1])
else:
self.energies.append(config[6][1])
else:
self.energies.append(config[6])
self.atom1.append(config[7])
self.atom2.append(config[8])
self.donors.append(config[9])
self.positives.append(config[10])
self.cations.append(config[11])
self.orientation.append(config[12])
self.ligands[ligand] += 1
def _hydrogen_bond(self, chain, residue, atom):
chain1 = ''
chain2 = ''
global n_or_o_donor
global h_donor
atom_name = atom.get_name()
if atom.fullname[1] in ['N', 'O'] or (atom_name == 'SG' and residue.resname == 'CYS'):
# search for atoms within a radius of 5.5 angstroms
neighbors = self.ns.search(atom.coord, 5.5)
for neighbor in neighbors:
neig_name = neighbor.get_name()
neig_res = neighbor.get_parent()
# Atoms HOH and 032 should not be included in the analysis
if neig_res.resname in ['HOH', '032']:
continue
# and a residue should not be analyzed with itself
if neig_res.id[1] == residue.id[1] or neig_name[0] == atom_name[0]:
continue
# pair analysis
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
if neighbor.fullname[1] in ['N', 'O'] or (neighbor.get_name() == 'SG' and neig_res.resname == 'CYS'):
distance = np.linalg.norm(atom.coord - neighbor.coord)
# checking the donor
if (atom_name[0] == 'N' or (atom_name in ['OG', 'OH', 'OG1', 'SG'] and residue.resname in list(
self.lighbdonor.keys()))) and (neig_name[0] == 'O' or (
neig_name in ['SD', 'ND1'] and neig_res.resname in list(self.lighbac.keys()))):
# here the donor will be the main atom
n_or_o_donor = atom
try:
#searching the hidrogen of the atom donor (getting the hydrogen with the shortest distance).
h_list = [a for a in residue if a.element == 'H']
h_distances = {}
for h_atom in h_list:
h_dist = np.linalg.norm(atom.coord - h_atom.coord)
h_distances[h_dist] = h_atom
min_h = min(list(h_distances.keys()))
h_donor = h_distances[min_h]
except:
raise Exception("Hydrogens not found, hydrogenate the pdb file first!")
elif (neig_name[0] == 'N' or (neig_name in ['OG', 'OH', 'OG1', 'SG'] and neig_res.resname in list(
self.lighbdonor.keys()))) and (atom_name[0] == 'O' or (
atom_name in ['SD', 'ND1'] and residue.resname in list(self.lighbac.keys()))):
# here the donor will be the neighbor atom
n_or_o_donor = neighbor
try:
#searching the hidrogen of the atom donor (getting the hydrogen with the shortest distance).
h_list = [a for a in neig_res if a.element == 'H']
h_distances = {}
for h_atom in h_list:
h_dist = np.linalg.norm(neighbor.coord - h_atom.coord)
h_distances[h_dist] = h_atom
min_h = min(list(h_distances.keys()))
h_donor = h_distances[min_h]
except:
raise Exception("Hydrogens not found, hydrogenate the pdb file first!")
terceiro_vetor = h_donor.get_vector()
neighbor_vector = neighbor.get_vector()
a_vector = atom.get_vector()
# the angle between H donor, donor and acceptor
angle = 0.0
if n_or_o_donor == atom:
angle = np.degrees(calc_angle(terceiro_vetor, a_vector, neighbor_vector))
else:
angle = np.degrees(calc_angle(terceiro_vetor, neighbor_vector, a_vector))
if 2.5 < distance <= 3.5 and angle <= 63.0:
# MC - Main Chain SC - Side Chain
if atom.name in ["N", "O"]:
chain1 = 'MC'
else:
chain1 = 'SC'
if neighbor.name in ['N', 'O']:
chain2 = 'MC'
else:
chain2 = 'SC'
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_res.get_parent().id}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
self.add_bond([
chain, residue, neig_res,
f"HBOND:{chain1}_{chain2}",
f"{distance:.3f}",
f"{angle:.3f}",
(f"{115.000:.3f}", f"{17.000:.3f}", f"{40.000:.3f}"),
atom_name,
neig_name,
f"{chain.id}:{str(n_or_o_donor.get_parent().id[1])}:_:{str(n_or_o_donor.get_parent().resname)}",
" ",
" ",
" "
], 'hb')
def _vanderwaals(self, chain, residue, atom):
chain1 = ''
chain2 = ''
# radius of the atoms present in the van der waals bonds
vdw_radii = {'C': 1.77, 'S': 1.89, 'N': 1.8, 'O': 1.4}
is_vdw = False
atom_name = atom.get_name()
if atom.fullname[1] in ['C', 'S', 'O', 'N']:
# search for atoms within a radius of 3.9 angstroms
neighbors = self.ns.search(atom.coord, 3.9)
for neighbor in neighbors:
is_vdw = False
neig_name = neighbor.get_name()
neig_res = neighbor.get_parent()
distance = np.linalg.norm(atom.coord - neighbor.coord)
# excluding some atoms that should not be in the bonds
if neig_res.id[1] == residue.id[1] or neig_name in ["CA", "CH2"] or atom_name in ["CA", "CH2"] or (
atom_name == 'C' and neig_name == 'C'):
continue
if neig_res.resname in ['HOH', '032']:
continue
# pair analysis
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
if neighbor.fullname[1] in ['C', 'S', 'O', 'N']:
# Check Chains
if atom.name in ["C", "S"]:
chain1 = 'MC'
else:
chain1 = 'SC'
if neighbor.name in ['C', 'S']:
chain2 = 'MC'
else:
chain2 = 'SC'
# checking if is vdw, following the ring intuition
if (atom.fullname[1] == "C" and neighbor.fullname[1] == "C") or (
atom.fullname[1] == "C" and neighbor.fullname[1] == "S") or (
atom.fullname[1] == "S" and neighbor.fullname[1] == "C"):
is_vdw = True
elif (atom_name[0] == "N" or atom_name[0] == "O") and neig_name[0] == "C":
if (residue.resname == 'GLN' and (atom_name == "OE1" or atom_name == "NE2")) or (
residue.resname == 'ASN' and (atom_name == "OD1" or atom_name == "ND2")):
is_vdw = True
elif (neig_name[0] == "N" or neig_name[0] == "O") and atom_name[0] == "C":
if (neig_res.resname == 'GLN' and (neighbor.name == "OE1" or neighbor.name == "NE2")) or (
neig_res.resname == 'ASN' and (neighbor.name == "OD1" or neighbor.name == "ND2")):
is_vdw = True
if is_vdw:
# the checking distance is obtained by substracting the atom's radii from the original distance
check_dist = distance - vdw_radii[atom.name[0]] - vdw_radii[neighbor.name[0]]
if check_dist <= 0.5:
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_res.get_parent().id}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
self.add_bond([
chain, residue, neig_res,
f"VDW:{chain1}_{chain2}",
f"{distance:.3f}",
" ",
f"{6.000:.3f}",
atom_name,
neig_name,
" ",
" ",
" ",
" "
], 'vdw')
def _dissulfide_bond(self, chain, residue, atom):
# identify the chains on dissulfide bond
chain1 = ''
chain2 = ''
atom_name = atom.get_name()
if atom_name[0] == 'S':
neighbors = self.ns.search(atom.coord, 3.5)
for neighbor in neighbors:
neig_res = neighbor.get_parent()
if neig_res.id[1] == residue.id[1]:
continue
# Check if the pair has already been analyzed
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
neig_name = neighbor.get_name()
neig_res = neighbor.get_parent()
distance = np.linalg.norm(atom.coord - neighbor.coord)
# if neighbor is also an S atom and the distance is less than 2.5
if neig_name[0] == 'S' and distance <= 2.5:
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_res.get_parent().id}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
self.add_bond([
chain, residue, neig_res,
f"SBOND:{chain1}_{chain2}",
f"{distance:.3f}",
" ",
f"{167.000:.3f}",
atom_name,
neig_name,
" ",
" ",
" ",
" "
], 'sbond')
def _salt_bridge(self, chain, residue, atom):
global ionic_donor
global h_donor
chain1 = ''
chain2 = ''
atom_name = atom.get_name()
# get only Asp, Glu, Arg, Lys and His residues
if residue.resname in ['ARG', 'LYS', 'HIS', 'ASP', 'GLU']:
analyzed_ionic = set()
# search for atoms within a radius of 4 angstroms
neighbors = self.ns.search(atom.coord, 4)
for neighbor in neighbors:
neig_res = neighbor.get_parent()
neig_name = neighbor.get_name()
# if the residues have the same IDs, it proceeds to the next iteration.
if neig_res.id[1] == residue.id[1]:
continue
# get only the CZ and NZ atoms
if atom_name in ['CZ', 'NZ']:
# Check if the pair has already been analyzed
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
#analyzed_ionic.add(pair)
if neig_res.resname in ['ARG', 'LYS', 'HIS', 'ASP', 'GLU']:
# positively charged amino acid (Arg, Lys, His) and a negatively charged residue (Asp or Glu)
if residue.resname in ['ARG', 'LYS', 'HIS'] and neig_res.resname in ['ASP', 'GLU']:
# atom is the positive
ionic_donor = atom
elif neig_res.resname in ['ARG', 'LYS', 'HIS'] and residue.resname in ['ASP', 'GLU']:
# neighbor is the positive
ionic_donor = neighbor
# Main Chain and Side Chain
chain1 = 'MC' if len(atom_name) == 1 else 'SC'
chain2 = 'MC' if len(neig_name) == 1 else 'SC'
# Calculing the distance and the angle
distance = np.linalg.norm(atom.coord - neighbor.coord)
angle = 0.0
if "CA" in residue:
angle = np.degrees(calc_angle(residue["CA"].get_vector(), atom.get_vector(), neighbor.get_vector()))
if distance <= 4.0:
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_res.get_parent().id}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
# conditions to print the coordinates or the atom, depending on the atom's name.
if atom_name in ['CZ', 'NZ']:
self.add_bond([
chain, residue, neig_res,
f"IONIC:{chain1}_{chain2}",
f"{distance:.3f}",
f"{angle:.3f}",
f"{20.000:.3f}",
atom_name,
f"{neighbor.get_coord()[0]:.3f},{neighbor.get_coord()[1]:.3f},{neighbor.get_coord()[2]:.3f}",
" ",
f"{chain.id}:{str(ionic_donor.get_parent().id[1])}:_:{str(ionic_donor.get_parent().resname)}",
" ",
" ",
], 'ionic')
elif atom_name not in ['CZ', 'NZ'] and neig_name in ['CZ', 'NZ']:
self.add_bond([
chain, residue, neig_res,
f"IONIC:{chain1}_{chain2}",
f"{distance:.3f}",
" ",
f"{20.000:.3f}",
f"{atom.get_coord()[0]:.3f},{atom.get_coord()[1]:.3f},{atom.get_coord()[2]:.3f}",
neig_name,
" ",
f"{chain.id}:{str(ionic_donor.get_parent().id[1])}:_:{str(ionic_donor.get_parent().resname)}",
" ",
" "
], 'ionic')
def _pi_stacking(self, chain, residue, atom):
neighbors = self.ns.search(atom.coord, 7.2)
amin = f'{chain.id} {residue.id[1]}'
orient_type = ''
for neighbor in neighbors:
neig_res = neighbor.get_parent()
neig_chain = neig_res.get_parent().id
neig_amin = f'{neig_chain} {neig_res.id[1]}'
if residue.get_resname() in ['TYR', 'PHE', 'TRP'] and neig_res.get_resname() in ['TYR', 'PHE', 'TRP']:
if (amin not in self.invalids and neig_amin not in self.invalids) & \
([amin, neig_amin] not in self.exclusions and [neig_amin, amin] not in self.exclusions):
coord_1 = np.array(self.aromatic_array[amin])
coord_2 = np.array(self.aromatic_array[neig_amin])
aromatic_distance = np.linalg.norm(coord_1 - coord_2)
if aromatic_distance < 5.5 and amin != neig_amin:
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
normal_1 = self.aromatic_normals[amin] / np.linalg.norm(self.aromatic_normals[amin])
normal_2 = self.aromatic_normals[neig_amin] / np.linalg.norm(self.aromatic_normals[neig_amin])
angle = np.arccos(np.clip(np.dot(normal_1, normal_2), -1.0, 1.0))
if angle > 50:
# Tshaped
orient_type = 'T'
elif 30 < angle < 50:
# Inter (stacked no parallel)
orient_type = 'I'
elif angle < 30:
# Parallel
orient_type = 'P'
chain1 = 'MC' if len(atom.get_name()) == 1 else 'SC'
chain2 = 'MC' if len(neighbor.get_name()) == 1 else 'SC'
coord_1 = f'{coord_1[0]:.3f},{coord_1[1]:.3f},{coord_1[2]:.3f}'
coord_2 = f'{coord_2[0]:.3f},{coord_2[1]:.3f},{coord_2[2]:.3f}'
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_res.get_parent().id}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
self.add_bond([
chain, residue, neig_res,
f"PIPISTACK:{chain1}_{chain2}",
f"{aromatic_distance:.3f}",
f"{angle:.3f}",
f"{9.4:.3f}",
coord_1,
coord_2,
" ",
" ",
" ",
f"{orient_type}"
], 'pi_stacking')
self.exclusions.append([amin, neig_amin])
def _pi_cation(self, chain, residue, atom):
neighbors = self.ns.search(atom.coord, 7.2)
ligctn = ['MG', 'CU', 'K', 'FE2', 'FE', 'NI', 'NA', 'MO1', 'MO3', 'MO4', 'MO5', 'MO6', 'MO7', 'MO8', 'MO9',
'NZ', 'NH2', 'NH1']
# tem que ver se chain
amin = f'{chain.id} {residue.id[1]}'
orient_type = ''
for neighbor in neighbors:
neig_res = neighbor.get_parent()
neig_chain = neig_res.get_parent().id
neig_amin = f'{neig_chain} {neig_res.id[1]}'
if residue.get_resname() in ['TYR', 'PHE', 'TRP'] and neighbor.get_name() in ligctn:
if (amin not in self.invalids and neig_amin not in self.invalids) & \
([amin, neig_amin] not in self.exclusions and [neig_amin, amin] not in self.exclusions):
coord_1 = np.array(self.aromatic_array[amin])
coord_2 = neighbor.get_coord()
aromatic_distance = np.linalg.norm(coord_1 - coord_2)
if 3.4 < aromatic_distance < 4.5 and amin != neig_amin:
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
chain1 = 'MC' if len(atom.get_name()) == 1 else 'SC'
chain2 = 'MC' if len(neighbor.get_name()) == 1 else 'SC'
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_chain}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
self.add_bond([
chain, residue, neig_res,
f"PICATION:{chain1}_{chain2}",
f"{aromatic_distance:.3f}",
" ",
f"{9.6:.3f}",
atom.get_name(),
neighbor.get_name(),
" ",
" ",
f"{neig_chain}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}",
"P"
], 'pi_cation')
self.exclusions.append([amin, neig_amin])
elif neig_res.get_resname() in ['TYR', 'PHE', 'TRP'] and atom.get_name in ligctn:
if (amin not in self.invalids and neig_amin not in self.invalids) & \
([amin, neig_amin] not in self.exclusions and [neig_amin, amin] not in self.exclusions):
coord_1 = neighbor.get_coord()
coord_2 = np.array(self.aromatic_array[amin])
aromatic_distance = np.linalg.norm(coord_1 - coord_2)
if 3.4 < aromatic_distance < 4.5 and amin != neig_amin:
pair = (residue, neig_res)
if pair in self.analyzed_pairs:
continue
else:
self.analyzed_pairs.add((neig_res, residue))
chain1 = 'MC' if len(atom.get_name()) == 1 else 'SC'
chain2 = 'MC' if len(neighbor.get_name()) == 1 else 'SC'
if self.multiple:
self.bonds_check.append((f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
f"{neig_chain}:{str(neig_res.id[1])}:_:{str(neig_res.resname)}"))
self.add_bond([
chain, residue, neig_res,
f"PICATION:{chain1}_{chain2}",
f"{aromatic_distance:.3f}",
" ",
f"{9.6:.3f}",
atom.get_name(),
neighbor.get_name(),
" ",
" ",
f"{chain.id}:{str(residue.id[1])}:_:{str(residue.resname)}",
"P"
], 'pi_cation')
self.exclusions.append([amin, neig_amin])
def Bonds(self):
for chain in self.structure.get_chains():
#self.analyzed_pairs = set()
for residue in chain:
if residue.resname in ['032', 'HOH']:
continue
for atom in residue:
atom_name = atom.get_name()
is_vdw = False
# Looking for HBOND
self._hydrogen_bond(chain, residue, atom)
# Looking for VDW
self._vanderwaals(chain, residue, atom)
# Looking for SBOND
self._dissulfide_bond(chain, residue, atom)
# Salt Bridges
self._salt_bridge(chain, residue, atom)
# Pi Stacking
self._pi_stacking(chain, residue, atom)
# Pi Cation
self._pi_cation(chain, residue, atom)
def analyse(self, bond, lig):
n_lines=0
"""
This function implements the analysis for the multiple mode, it checks if the same pair with the same bond exists
more than once and takes only the one with the shortest distance between theses repeated bonds, allowing only
1 type of bond per pair of residue.
"""
for pair in self.bonds_check:
pair_dist, pair_idx = [], []
n_lines=0
for line in range(len(self.nodes_id1)):
# getting the pair_distance and index and adding to pair_dist and pair_idx
if (pair == (self.nodes_id1[line], self.nodes_id2[line]) and (bond in self.bonds[line])):
pair_dist.append(self.distances[line])
pair_idx.append(line)
if len(pair_dist) > 1:
# get the min distance in the pair_dist and her index
min_idx = np.argmin(pair_dist)
min_pair = pair_idx[min_idx]
for i in pair_idx:
if i != min_pair:
i -= n_lines
self.nodes_id1.pop(i)
self.nodes_id2.pop(i)
self.donors.pop(i)
self.angles.pop(i)
self.energies.pop(i)
self.bonds.pop(i)
self.distances.pop(i)
self.atom1.pop(i)
self.atom2.pop(i)
self.positives.pop(i)
self.cations.pop(i)
self.orientation.pop(i)
self.ligands[lig] -= 1
n_lines+=1
def multiple_mode(self):
# implement the analyse function for each bond in software
bonds = [("HBOND", "hb"), ("VDW", "vdw"), ("SBOND", "sbond"), ("IONIC", "ionic"),
("PIPISTACK", "pi_stacking"), ("PICATION", "pi_cation")]
for b in bonds:
self.analyse(b[0], b[1])
# functions to save in file and print on terminal (optional)
def to_file(self):
self.Bonds()
if self.multiple:
self.multiple_mode()
colunas = ["NodeId1", "Interaction", "NodeId2", "Distance", "Angle", "Energy", "Atom1", "Atom2", "Donor", "Positive", "Cation", "Orientation"]
data = pd.DataFrame(list(zip(self.nodes_id1, self.bonds, self.nodes_id2, self.distances,
self.angles, self.energies, self.atom1, self.atom2, self.donors, self.positives, self.cations, self.orientation)), columns=colunas)
data.to_csv(f'./{self.name}_edges.txt', sep='\t', index=False)
print(self.ligands)
def print_output(self, slow=False):
self.Bonds()
if self.multiple:
self.multiple_mode()
print(len(self.nodes_id1), len(self.donors))
time.sleep(2)
for n in range(len(self.nodes_id1)):
try:
print(
f"{self.nodes_id1[n]}\t{self.bonds[n]}\t{self.nodes_id2[n]}\t{self.distances[n]}"
f"\t{self.angles[n]}\t\t{self.energies[n]}\t\t{self.atom1[n]}\t{self.atom2[n]}\t{self.donors[n]}\t{self.orientation[n]}")
if slow:
time.sleep(0.01)
except Exception as e:
print(e)
print(
f"{self.nodes_id1[n]}\t{self.bonds[n]}\t{self.nodes_id2[n]}\t{self.distances[n]}\t{self.angles[n]}"
f"\t\t{self.energies[n]}\t\t{self.atom1[n]}\t{self.atom2[n]}\t{self.donors[n]}\t{self.orientation[n]}")
print(self.ligands)