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feature_data.py
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# from Bio.PDB import *
import random
import freesasa
import os
import pickle
import itertools
from Bio.PDB import PDBParser, PDBIO, Select
import numpy as np
import math
from Bio.PDB.DSSP import DSSP
import glob
with open('./Data/DBD/data/ubToB_A.pkl', 'rb') as f:
test_list = pickle.load(f)
with open('./Data/DBD/data/train462_list.pkl', 'rb') as f:
ubToB_list = pickle.load(f)
fasta_dict = pickle.load(open('./Data/DBD/data/seq_single_ub_A.pkl', 'rb'))
# fasta_dict = pickle.load(open('./Data/DBD/data/label_b.pkl', 'rb'))
def onehot_dict(list, seq_dict):
AA_index = {j: i for i, j in enumerate("ACDEFGHIKLMNPQRSTVWY")}
onehot_dict = {}
for i in list:
protein = i[0:4]
c = i.split('_')[1].upper()
l = i.split('_')[2]
onehot = []
for ci in c:
for AA in seq_dict[protein + '_' + ci + '_' + l]:
zero = [0] * 20
zero[AA_index[AA]] = 1
onehot.append(zero)
onehot_dict[i] = onehot
f = open('./Data/DBD/data/feature_data/One_hot_dict.pkl', 'wb')
pickle.dump(onehot_dict, f)
def PSSM_dict(data_list, PSSM_dir='./Data/DBD/data/feature/pssm/'):
PSSM_dict = {}
# max = np.array(
# [7., 9., 9., 9., 12., 9., 8., 8., 12., 8., 7., 9., 11., 10., 9., 8., 8., 13., 10., 8.])
# min = np.array(
# [-10., - 12., - 12., - 12., - 11., - 11., - 12., - 12., - 12., - 12., - 12., - 12., - 11., - 11.,
# - 12., - 11., - 10., - 12., - 11., - 11.])
max = np.array([8., 9., 9., 9., 12., 9., 8., 8., 12., 9., 7., 8., 11.,
10., 9., 8., 8., 13., 10., 8.])
min = np.array([-10., -11., -12., -12., -11., -10., -11., -11., -11., -10., -11.,
-11., -10., -11., -12., -11., -10., -11., -10., -11.])
for i in data_list:
protein = i[0:4]
c = i.split('_')[1].upper()
l = i.split('_')[2]
PSSM_matrix = []
for ci in c:
pdbid = f'{protein}_{ci}_{l}'
try:
PSSM_file = open(f'{PSSM_dir}{pdbid}.pssm', 'r')
for line in PSSM_file:
if line != None and len(line.split()) > 40:
PSSM_line = line.split()[2:22]
PSSM_line = list(map(float, PSSM_line))
PSSM_line = ((np.array(PSSM_line) - min) / (max - min)).tolist()
PSSM_matrix.append(PSSM_line)
except:
# PSSM_matrix = []
for j in range(len(fasta_dict[pdbid])):
PSSM_line = [0.0]*20
PSSM_matrix.append(PSSM_line)
# PSSM_dict[pdbid] = PSSM_matrix
PSSM_dict[i] = PSSM_matrix
f = open('./Data/DBD/data/feature_data/PSSM_dict.pkl', 'wb')
pickle.dump(PSSM_dict, f)
def HMM_dict(seq_list, hmm_dir, feature_dir):
hmm_dict = {}
for seqid in seq_list:
pdbid = seqid[:4]
chian = seqid[5:]
hmm_feature = []
for c in chian:
file = pdbid + '_' + c + '.hhm'
with open(hmm_dir + file, 'r') as fin:
fin_data = fin.readlines()
hhm_begin_line = 0
hhm_end_line = 0
for i in range(len(fin_data)):
if '#' in fin_data[i]:
hhm_begin_line = i + 5
elif '//' in fin_data[i]:
hhm_end_line = i
feature = np.zeros([int((hhm_end_line - hhm_begin_line) / 3), 20])
axis_x = 0
for i in range(hhm_begin_line, hhm_end_line, 3):
line1 = fin_data[i].split()[2:-1]
line2 = fin_data[i + 1].split()
axis_y = 0
for j in line1:
if j == '*':
feature[axis_x][axis_y] = 9999 / 10000.0
else:
feature[axis_x][axis_y] = float(j) / 10000.0
axis_y += 1
# for j in line2:
# if j == '*':
# feature[axis_x][axis_y] = 9999 / 10000.0
# else:
# feature[axis_x][axis_y] = float(j) / 10000.0
# axis_y += 1
axis_x += 1
feature = (feature - np.min(feature)) / (np.max(feature) - np.min(feature))
hmm_feature.append(feature)
hmm_dict[seqid] = np.concatenate(hmm_feature, dim=0)
with open(feature_dir + 'HMM_dict.pkl', 'wb') as f:
pickle.dump(hmm_dict, f)
return
def RSA_dict(data_list):
RSA_dict = {}
for i in data_list:
protein = i[0:4]
c = i.split('_')[1].upper()
l = i.split('_')[2]
structure = freesasa.Structure(f'./Data/DBD/data/ub_pdb/{i}.pdb')
result = freesasa.calc(structure, freesasa.Parameters(
{'algorithm': freesasa.LeeRichards, 'n-slices': 100, 'probe-radius': 1.4}))
residueAreas = result.residueAreas()
RSA = []
for ci in c:
pdbid = f'{protein}_{ci}_{l}'
if ci == '*':
ci = ' '
for r in residueAreas[ci].keys():
RSA_AA = []
RSA_AA.append(min(1, residueAreas[ci][r].relativeTotal))
RSA_AA.append(min(1, residueAreas[ci][r].relativePolar))
RSA_AA.append(min(1, residueAreas[ci][r].relativeApolar))
RSA_AA.append(min(1, residueAreas[ci][r].relativeMainChain))
if math.isnan(residueAreas[ci][r].relativeSideChain):
RSA_AA.append(0)
else:
RSA_AA.append(min(1, residueAreas[ci][r].relativeSideChain))
RSA.append(RSA_AA)
RSA_dict[i] = RSA
f = open('./Data/DBD/data/feature_data/RSA_dict.pkl', 'wb')
pickle.dump(RSA_dict, f)
class ChianSelect(Select):
def __init__(self, chain_letter):
self.chain_letter = chain_letter
def accept_chain(self, chain):
if chain.get_id() in self.chain_letter:
return True
else:
return False
def Single_PDB(data_list):
for i in data_list:
protein = i[0:4]
c = i[5:].upper()
p = PDBParser(QUIET=1)
pdb = p.get_structure(protein, "./Data/test34/" + i + '.pdb')
io = PDBIO()
io.set_structure(pdb)
chain_list = [c]
for ci in c:
chain = [ci]
io.save('./Data/single_pdb/' + protein + '_' + ci + '.pdb', ChianSelect(chain))
class CA_Select(Select):
def accept_residue(self, residue):
return 1 if residue.id[0] == " " else 0
def accept_atom(self, atom):
if atom.get_name() == 'CA':
return True
else:
return False
def CA_PDB(data_list):
for i in data_list:
protein = i[0:4]
c = i[5:].upper()
p = PDBParser(QUIET=1)
for ci in c:
pdb = p.get_structure(protein, "./Data/single_pdb/" + i + '.pdb')
io = PDBIO()
io.set_structure(pdb)
io.save('./Data/updata/pdb/CA_pdb/' + protein + '_' + ci + '.pdb', CA_Select())
def AA_property(data_list, seq_dict):
Side_Chain_Atom_num = {'A': 5.0, 'C': 6.0, 'D': 8.0, 'E': 9.0, 'F': 11.0, 'G': 4.0, 'H': 10.0, 'I': 8.0, 'K': 9.0,
'L': 8.0, 'M': 8.0, 'N': 8.0, 'P': 7.0, 'Q': 9.0, 'R': 11.0, 'S': 6.0, 'T': 7.0, 'V': 7.0,
'W': 14.0, 'Y': 12.0}
Side_Chain_Charge_num = {'A': 0.0, 'C': 0.0, 'D': -1.0, 'E': -1.0, 'F': 0.0, 'G': 0.0, 'H': 1.0, 'I': 0.0, 'K': 1.0,
'L': 0.0, 'M': 0.0, 'N': 0.0, 'P': 0.0, 'Q': 0.0, 'R': 1.0, 'S': 0.0, 'T': 0.0, 'V': 0.0,
'W': 0.0, 'Y': 0.0}
Side_Chain_hydrogen_bond_num = {'A': 2.0, 'C': 2.0, 'D': 4.0, 'E': 4.0, 'F': 2.0, 'G': 2.0, 'H': 4.0, 'I': 2.0,
'K': 2.0, 'L': 2.0, 'M': 2.0, 'N': 4.0, 'P': 2.0, 'Q': 4.0, 'R': 4.0, 'S': 4.0,
'T': 4.0, 'V': 2.0, 'W': 3.0, 'Y': 3.0}
Side_Chain_pKa = {'A': 7.0, 'C': 7.0, 'D': 3.65, 'E': 3.22, 'F': 7.0, 'G': 7.0, 'H': 6.0, 'I': 7.0, 'K': 10.53,
'L': 7.0, 'M': 7.0, 'N': 8.18, 'P': 7.0, 'Q': 7.0, 'R': 12.48, 'S': 7.0, 'T': 7.0, 'V': 7.0,
'W': 7.0, 'Y': 10.07}
Hydrophobicity = {'A': 1.8, 'C': 2.5, 'D': -3.5, 'E': -3.5, 'F': 2.8, 'G': -0.4, 'H': -3.2, 'I': 4.5, 'K': 3.9,
'L': 3.8, 'M': 1.9, 'N': -3.5, 'P': -1.6, 'Q': -3.5, 'R': -4.5, 'S': -0.8, 'T': -0.7, 'V': 4.2,
'W': -0.9, 'Y': -1.3}
AA_property_dict = {}
for i in data_list:
protein = i[0:4]
c = i.split('_')[1].upper()
l = i.split('_')[2]
AA_protein = []
for ci in c:
for AA in seq_dict[protein + '_' + ci + '_' + l]:
AA_AA = []
AA_AA.append(Side_Chain_Atom_num[AA])
AA_AA.append(Side_Chain_Charge_num[AA])
AA_AA.append(Side_Chain_hydrogen_bond_num[AA])
AA_AA.append(Side_Chain_pKa[AA])
AA_AA.append(Hydrophobicity[AA])
AA_protein.append(AA_AA)
AA_property_dict[i] = AA_protein
f = open('./Data/DBD/data/feature_data/AA_property_dict.pkl', 'wb')
pickle.dump(AA_property_dict, f)
def DSSP_dict(data_list, seq_dict):
SS_dict = {'H': 0, 'B': 1, 'E': 2, 'G': 3, 'I': 4, 'T': 5, 'S': 6, '-': 7}
dssp_dict = {}
for data in data_list:
protein = data[0:4]
c = data.split('_')[1].upper()
l = data.split('_')[2]
dssp_feature = []
for ci in c:
pdbid = protein + '_' + ci + '_' + l
if ci == '*':
ci = ' '
ref_seq = seq_dict[pdbid]
p = PDBParser(QUIET=1)
structure = p.get_structure(protein, './Data/DBD/data/ub_pdb/' + data + '.pdb')
model = structure[0]
dssp = DSSP(model, './Data/DBD/data/ub_pdb/' + data + '.pdb',
dssp='/mnt/data0/Hanjy/.conda/envs/pytorch/bin/mkdssp')
key_list = []
for i in dssp.keys():
if i[0] == ci:
key_list.append(i)
dssp_matrix = []
seq = ""
for i in key_list:
SS = dssp[i][2]
AA = dssp[i][1]
seq += AA
phi = dssp[i][4]
psi = dssp[i][5]
raw = []
raw.append(np.sin(phi * (np.pi / 180)))
raw.append(np.sin(psi * (np.pi / 180)))
raw.append(np.sin(phi * (np.pi / 180)))
raw.append(np.cos(psi * (np.pi / 180)))
ss_raw = [0] * 9
ss_raw[SS_dict[SS]] = 1
raw.extend(ss_raw)
dssp_matrix.append(raw)
pad = []
pad.append(np.sin(360 * (np.pi / 180)))
pad.append(np.sin(360 * (np.pi / 180)))
pad.append(np.cos(360 * (np.pi / 180)))
pad.append(np.cos(360 * (np.pi / 180)))
ss_pad = [0] * 9
ss_pad[-1] = 1
pad.extend(ss_pad)
pad_dssp_matrix = []
p_ref = 0
for i in range(len(seq)):
while p_ref < len(ref_seq) and seq[i] != ref_seq[p_ref]:
pad_dssp_matrix.append(pad)
p_ref += 1
if p_ref < len(ref_seq): # aa matched
pad_dssp_matrix.append(dssp_matrix[i])
p_ref += 1
if len(pad_dssp_matrix) != len(ref_seq):
for i in range(len(ref_seq) - len(pad_dssp_matrix)):
pad_dssp_matrix.append(pad)
dssp_feature.extend(pad_dssp_matrix)
dssp_dict[data] = dssp_feature
f = open('./Data/DBD/data/feature_data/DSSP_dict.pkl', 'wb')
pickle.dump(dssp_dict, f)
def Dist_adj(data_list):
def dictance(xyz, position):
xyz = xyz - xyz[position]
dictance = np.sqrt(xyz[:, 0] ** 2 + xyz[:, 1] ** 2 + xyz[:, 2] ** 2).tolist()
return dictance
distance_dict = {}
for data in data_list:
protein = data[0:4]
c = data.split('_')[1].upper()
l = data.split('_')[2]
p = PDBParser(QUIET=1)
structure = p.get_structure(protein, "./Data/DBD/data/CA_pdb/" + data + '.pdb')
# for ci in c:
# structure = protein.get_structure(protein, "./case_study/CA_PDB/" + protein + '_' + ci + '.pdb')
distance_list = []
chain_list = []
for ci in c:
# pdbid = protein + '_' + ci + '_' + l
if ci == '0':
ci = ' '
try:
for residue in structure[0][ci]:
for atom in residue:
chain_list.append(atom.get_vector().get_array())
except:
for residue in structure[0][' ']:
for atom in residue:
chain_list.append(atom.get_vector().get_array())
for i, center in enumerate(chain_list):
distance_list.append(dictance(chain_list, i))
distance_dict[data] = distance_list
f = open('./Data/DBD/data/feature_data/Dist_dict.pkl', 'wb')
pickle.dump(distance_dict, f)
def CX_DPX(data_list, path="./Data/DBD/data/feature/CX_DPX"):
CX_DPX_dict = {}
for i in data_list:
protein = i[0:4]
c = i.split('_')[1].upper()
l = i.split('_')[2]
# for ci in c:
if c == '*':
i = protein + '___' + l
CX_DPX_protein = []
try:
fname = glob.glob(path + "/" + i + "*.tbl")[0]
except:
print(i)
f = open(fname, 'r')
for line in f:
if line.split() != []:
if line.split()[0] in c:
AA_CX_DPX = list(map(float, line.split()[3:]))
CX_DPX_protein.append(AA_CX_DPX)
i = protein + '_' + c + '_' + l
CX_DPX_dict[i] = CX_DPX_protein
f = open('./Data/DBD/data/feature_data/CX_DPX.pkl', 'wb')
pickle.dump(CX_DPX_dict, f)
#onehot_dict(test_list, fasta_dict)
#PSSM_dict(test_list)
RSA_dict(test_list)
# Single_PDB(test_list)
# CA_PDB(test_list)
#AA_property(test_list, fasta_dict)
DSSP_dict(test_list, fasta_dict)
# Dist_adj(test_list)
print('done!!')