-
Notifications
You must be signed in to change notification settings - Fork 1
/
data_processor_polar.py
268 lines (241 loc) · 11 KB
/
data_processor_polar.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 20 13:19:00 2019
@author: Saint8312
"""
import numpy as np
import pandas as pd
import sys, os
import time
import multiprocessing
import itertools
import pickle
import data_checker
import data_multi_processor
def hydrophobic_acid_patch_interactions(paramlist):
'''
get the coordinates of CAs and then classify them based on the type of amino acid (hydrophobic, charged polar/acid), and then calculate the euclidean-heaviside as usual
- hydrophobics = ['ALA','VAL','ILE','LEU','MET','PHE','TYR','TRP']
- acids = ['ARG','HIS','LYS','ASP','GLU']
'''
path = paramlist[0]
id_name = paramlist[1]
cutoff = paramlist[2]
print("processing ",id_name)
path_file = path+'/'+id_name
l =[]
with open(path_file, 'r') as f:
for line in f:
if line.startswith('ATOM'):
clean_line = (line.rstrip()).split()
#check for alignment mistakes within data, a row with spacing alignment error has 11 length after splitted by whitespace
if len(clean_line) == 11:
#split the 2nd last column by the 4th index (this inference is according to PDB file formatting)
split = [clean_line[-2][:4], clean_line[-2][4:]]
clean_line[-2] = split[1]
clean_line.insert(-2, split[0])
#check if the chain identifier is misaligned
if len(clean_line[4])>1:
split = [clean_line[4][0], clean_line[4][1:]]
clean_line[4] = split[0]
clean_line.insert(5, split[1])
#check if coordinate data collumns are collided (most likely happens between x and y coor)
while len(clean_line[6])>=13:
split = [clean_line[6][:-8], clean_line[6][-8:]]
# last_elem = clean_line.pop()
# clean_line[-1] = last_elem
clean_line.insert(6, split[0])
clean_line[7] = split[1]
if len(clean_line[7])>=13:
split = [clean_line[7][:-8], clean_line[7][-8:]]
last_elem = clean_line.pop()
clean_line[-1] = last_elem
clean_line.insert(7, split[0])
clean_line[8] = split[1]
l.append(clean_line)
elif line.startswith('TER'):
clean_line = (line.rstrip()).split()
l.append(clean_line)
elif line.startswith('ENDMDL'):
break
df_atoms = (pd.DataFrame(l)).rename(columns={0:'record', 6:'x_coor', 7:'y_coor', 8:'z_coor', 11:'atom_type', 2:'atom_name', 3:'amino_acid_type'})
#dataframe splitter:
l_df = []
last_idx = 0
for idx in df_atoms.index[df_atoms['record'] == 'TER'].tolist():
l_df.append(df_atoms.iloc[last_idx:idx])
last_idx = idx+1
#hydrophobics and acids types of amino acids
hydrophobics = ['ALA','VAL','ILE','LEU','MET','PHE','TYR','TRP']
acids = ['ARG','HIS','LYS','ASP','GLU']
#select the carbon alpha of atoms based on the amino acid types
hydrophobics_patches = []
for i in range(len(l_df)):
mol_patch=l_df[i].set_index(['amino_acid_type'])
hydrophobics_patches.append(mol_patch.loc[ (mol_patch.index.isin(hydrophobics)) & (mol_patch['atom_name'] == 'CA') ])
acid_patches = []
for i in range(len(l_df)):
mol_patch=l_df[i].set_index(['amino_acid_type'])
acid_patches.append(mol_patch.loc[ (mol_patch.index.isin(acids)) & (mol_patch['atom_name'] == 'CA') ])
patches = [hydrophobics_patches, acid_patches]
#create the combination of protein patches interactions
x_vector = np.zeros(2)
patch_idx = 0
for patch in patches:
sum_interactions = 0
comb_ = itertools.combinations(patch, 2)
for c_ in list(comb_):
#function to calculate the distance-cutoff between CAs of two protein patches:
coors_0 = (c_[0][["x_coor", "y_coor", "z_coor"]]).to_numpy(dtype=float)
coors_1 = (c_[1][["x_coor", "y_coor", "z_coor"]]).to_numpy(dtype=float)
product_coors = np.array(list(itertools.product(coors_0, coors_1)))
# if pool:
# euclid_dists = pool.map(data_multi_processor.f_euc_mp, product_coors)
# euclid_dists = np.array(list(euclid_dists))
# paramlist = list(itertools.product(euclid_dists, [cutoff]))
# heavisides = pool.map(data_multi_processor.f_heaviside_mp, paramlist)
# heavisides = np.array(list(heavisides))
# else:
euclid_dists = np.array(list(map(data_multi_processor.f_euc_mp, product_coors)))
paramlist = list(itertools.product(euclid_dists, [cutoff]))
heavisides = np.array(list(map(data_multi_processor.f_heaviside_mp, paramlist)))
sum_interactions += np.sum(heavisides)
x_vector[patch_idx] = sum_interactions
patch_idx+=1
return {'id':id_name, 'h_a_vector':x_vector}
if __name__ == '__main__':
'''
create subset matrices from the dataset, the default matrices should be (N,81) where N is the total data
the subset will be (N, 16), taking only [C,N,O,S] atom types
'''
dataset = data_checker.data_load(os.getcwd()+'/dataset.pkl')
saved_id = [d['id'] for d in dataset]
# print('processed protein IDs = ',saved_id, print(len(saved_id)))
sorted_dataset = sorted(dataset, key = lambda k:k['id'])
atom_types = ['C','N','O','F','P','S','Cl','Br','I']
subset_atom_types = ['C','N','O','S']
subset_exclude = list(set(atom_types)-set(subset_atom_types))
paramlist = list(itertools.product(atom_types, atom_types))
# print(paramlist)
idx_l = []
for i in range(len(paramlist)):
result = not any(elem in paramlist[i] for elem in subset_exclude)
if result:
idx_l.append(i)
for i in range(len(sorted_dataset)):
sorted_dataset[i]['x_vector'] = sorted_dataset[i]['x_vector'][idx_l]
'''
hydrophobic and acid patches interactions calculation
'''
path = 'C:/Users/beryl/Documents/Computational Science/Kanazawa/Thesis/Dataset/PP'
complex_files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
complexes = complex_files
cutoff = 12
#
filename = 'h_a_vec.pkl'
# pool = multiprocessing.Pool()
#start of the process
start_time = time.time()
# paramlist = list(itertools.product([path], complexes, [cutoff]))
# h_a_vec = pool.map(hydrophobic_acid_patch_interactions, paramlist)
# h_a_vec = list(h_a_vec)
#check if id is already existed within file, if yes, skip it
data = []
try:
with open(filename, 'rb') as fr:
print(filename, 'is found')
try:
while True:
data.append(pickle.load(fr))
except EOFError:
pass
except FileNotFoundError:
print('File is not found')
saved_ids = [d['id'] for d in data]
try:
i=0
for id_file in complexes:
if id_file in saved_ids:
continue
else:
print("start of process for ID :",id_file)
paramlist = [path, id_file, cutoff]
h_a_vec = hydrophobic_acid_patch_interactions(paramlist)
print("ID : ", id_file)
print('value of x vector (R^N) = ', h_a_vec)
with open(filename, 'ab') as f:
pickle.dump(h_a_vec, f)
i+=1
except KeyboardInterrupt:
print('interrupted !!')
end_time = time.time()
print("the number of protein processed in current run = ",i)
print('time elapsed =',end_time-start_time,'seconds')
'''
data checker
'''
data = data_checker.data_load(filename)
print(data, len(data))
# id_name = '4gxu.ent.pdb'
# path_file = path+'/'+id_name
# l =[]
# with open(path_file, 'r') as f:
# for line in f:
# if line.startswith('ATOM'):
# clean_line = (line.rstrip()).split()
# #check for alignment mistakes within data, a row with spacing alignment error has 11 length after splitted by whitespace
# if len(clean_line) == 11:
# #split the 2nd last column by the 4th index (this inference is according to PDB file formatting)
# split = [clean_line[-2][:4], clean_line[-2][4:]]
# clean_line[-2] = split[1]
# clean_line.insert(-2, split[0])
# #check if the chain identifier is misaligned
# if len(clean_line[4])>1:
# split = [clean_line[4][0], clean_line[4][1:]]
# clean_line[4] = split[0]
# clean_line.insert(5, split[1])
# #check if coordinate data collumns are collided (most likely happens between x and y coor)
# while len(clean_line[6])>=13:
# split = [clean_line[6][:-8], clean_line[6][-8:]]
## last_elem = clean_line.pop()
## clean_line[-1] = last_elem
# clean_line.insert(6, split[0])
# clean_line[7] = split[1]
# if len(clean_line[7])>=13:
# split = [clean_line[7][:-8], clean_line[7][-8:]]
# last_elem = clean_line.pop()
# clean_line[-1] = last_elem
# clean_line.insert(7, split[0])
# clean_line[8] = split[1]
# l.append(clean_line)
# elif line.startswith('TER'):
# clean_line = (line.rstrip()).split()
# l.append(clean_line)
# elif line.startswith('ENDMDL'):
# break
# print(l[34338])
# df_atoms = (pd.DataFrame(l)).rename(columns={0:'record', 6:'x_coor', 7:'y_coor', 8:'z_coor', 11:'atom_type', 2:'atom_name', 3:'amino_acid_type'})
#
# #dataframe splitter:
# l_df = []
# last_idx = 0
# for idx in df_atoms.index[df_atoms['record'] == 'TER'].tolist():
# l_df.append(df_atoms.iloc[last_idx:idx])
# last_idx = idx+1
#
# #hydrophobics and acids types of amino acids
# hydrophobics = ['ALA','VAL','ILE','LEU','MET','PHE','TYR','TRP']
# acids = ['ARG','HIS','LYS','ASP','GLU']
#
# #select the carbon alpha of atoms based on the amino acid types
# hydrophobics_patches = []
# for i in range(len(l_df)):
# mol_patch=l_df[i].set_index(['amino_acid_type'])
# hydrophobics_patches.append(mol_patch.loc[ (mol_patch.index.isin(hydrophobics)) & (mol_patch['atom_name'] == 'CA') ])
# print(mol_patch.loc[ (mol_patch.index.isin(hydrophobics)) & (mol_patch['atom_name'] == 'CA') ][['x_coor','y_coor','z_coor']])
#
# acid_patches = []
# for i in range(len(l_df)):
# mol_patch=l_df[i].set_index(['amino_acid_type'])
# acid_patches.append(mol_patch.loc[ (mol_patch.index.isin(acids)) & (mol_patch['atom_name'] == 'CA') ])
# print()