-
Notifications
You must be signed in to change notification settings - Fork 92
/
feature.py
263 lines (233 loc) · 10.2 KB
/
feature.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
import utils
import argparse
import itertools
import numpy as np
from pathlib import Path
import scipy.io as sio
from scipy.spatial.distance import pdist, squareform
def make_crops(seq_file):
target_line, *seq_line = seq_file.read_text().split('\n')
target = seq_file.stem
suffix = seq_file.suffix
target_seq = ''.join(seq_line)
for domain in utils.generate_domains(target, target_seq):
name = domain['name']
if name == target: continue
crop_start, crop_end = domain["description"]
seq = target_seq[crop_start-1:crop_end]
(seq_file.parent / f'{name}{suffix}').write_text(f'>{name}\n{seq}')
def sequence_to_onehot(seq):
mapping = {aa: i for i, aa in enumerate('ARNDCQEGHILKMFPSTWYVX')}
num_entries = max(mapping.values()) 1
one_hot_arr = np.zeros((len(seq), num_entries), dtype=np.float32)
for aa_index, aa_type in enumerate(seq):
aa_id = mapping[aa_type]
one_hot_arr[aa_index, aa_id] = 1
return one_hot_arr
def extract_hmm_profile(hhm_file, sequence, asterisks_replace=0.0):
"""Extracts information from the hmm file and replaces asterisks."""
profile_part = hhm_file.split('#')[-1]
profile_part = profile_part.split('\n')
whole_profile = [i.split() for i in profile_part]
# This part strips away the header and the footer.
whole_profile = whole_profile[5:-2]
gap_profile = np.zeros((len(sequence), 10))
aa_profile = np.zeros((len(sequence), 20))
count_aa = 0
count_gap = 0
for line_values in whole_profile:
if len(line_values) == 23:
# The first and the last values in line_values are metadata, skip them.
for j, t in enumerate(line_values[2:-1]):
aa_profile[count_aa, j] = (2**(-float(t) / 1000.) if t != '*' else asterisks_replace)
count_aa = 1
elif len(line_values) == 10:
for j, t in enumerate(line_values):
gap_profile[count_gap, j] = (2**(-float(t) / 1000.) if t != '*' else asterisks_replace)
count_gap = 1
elif not line_values:
pass
else:
raise ValueError('Wrong length of line %s hhm file. Expected 0, 10 or 23'
'got %d'%(line_values, len(line_values)))
hmm_profile = np.hstack([aa_profile, gap_profile])
assert len(hmm_profile) == len(sequence)
return hmm_profile
def read_aln(aln_file):
aln = []
aln_id = []
seq = ''
for line in aln_file.open():
line = line.strip()
if line[0] == '>':
aln_id.append(line)
if seq: aln.append(list(seq))
seq = ''
else:
seq = line
if seq: aln.append(list(seq))
aln = np.array(aln)
return aln, aln_id
def write_aln(aln, aln_id, out_file):
with out_file.open('w') as f:
for i in range(len(aln_id)):
seq = ''.join(aln[i])
f.write(f'{aln_id[i]}\n{seq}\n')
def sequence_weights(sequence_matrix):
num_rows, num_res = sequence_matrix.shape
cutoff = 0.62 * num_res
weights = np.ones(num_rows, dtype=np.float32)
for i in range(num_rows):
for j in range(i 1, num_rows):
similarity = (sequence_matrix[i] == sequence_matrix[j]).sum()
if similarity > cutoff:
weights[i] = 1
weights[j] = 1
return 1.0 / weights
def calculate_f(align, theta=0.38):
M, N = align.shape
q = align.max()
# W: 1*M
W = 1 / (1 np.sum(squareform(pdist(align,'hamming')<theta),0))
Meff = np.sum(W)
# cache a align residue table: q*N*M
residue_table = np.zeros((q, N, M))
for i in range(q):
residue_table[i] = align.T == i 1
# fi: N*q
fi = np.array([np.sum(W * residue_table[i], 1) for i in range(q)]).T / Meff
# this cost most time!
fij = np.empty((N, N, q, q))
for (A, B) in itertools.product(range(q), range(q)):
for (i, j) in itertools.combinations(range(N), 2):
t = np.sum(W * residue_table[A][i].T * residue_table[B][j].T)
fij[i,j,A,B] = t
fij[j,i,B,A] = t
fij /= Meff
for i in range(N):
fij[i,i] = np.eye(q) * fi[i]
del residue_table
return fi, fij, Meff
def calculate_MI(fi, fij):
N, q = fi.shape
MI = np.zeros((N, N, 1), dtype=np.float32)
for i, j in itertools.combinations(range(N),2):
m = 0
for (A, B) in itertools.product(range(q), range(q)):
if fij[i,j,A,B] > 0:
m = fij[i,j,A,B] * np.log( fij[i,j,A,B] / fi[i,A] / fi[j,B] )
MI[i,j,0] = m
MI[j,i,0] = m
return MI
def feature_generation(seq_file, out_file):
target_line, *seq_line = seq_file.read_text().split('\n')
target = seq_file.stem
target_seq = ''.join(seq_line)
data_dir = seq_file.parent
dataset = []
for domain in utils.generate_domains(target, target_seq):
name = domain['name']
crop_start, crop_end = domain["description"]
seq = target_seq[crop_start-1:crop_end]
L = len(seq)
hhm_file = data_dir / f'{name}.hhm'
fas_file = data_dir / f'{name}.fas'
aln_file = data_dir / f'{name}.aln'
mat_file = data_dir / f'{name}.mat'
if aln_file.exists():
aln, _ = read_aln(aln_file)
else:
aln, aln_id = read_aln(fas_file)
aln = aln[:, aln[0] != '-']
write_aln(aln, aln_id, aln_file)
exit()
if mat_file.exists():
mat = sio.loadmat(mat_file)
pseudo_bias = np.float32(mat['pseudo_bias'])
pseudo_frob = np.float32(np.expand_dims(mat['pseudo_frob'], -1))
pseudolikelihood = np.float32(mat['pseudolikelihood'])
else:
pseudo_bias = np.zeros((L, 22), dtype=np.float32)
pseudo_frob = np.zeros((L, L, 1), dtype=np.float32)
pseudolikelihood = np.zeros((L, L, 484), dtype=np.float32)
gap_count = np.float32(aln=='-')
gap_matrix = np.expand_dims(np.matmul(gap_count.T, gap_count) / aln.shape[0], -1)
mapping = {aa: i for i, aa in enumerate('ARNDCQEGHILKMFPSTWYVX-')}
seq_weight = sequence_weights(aln)
hhblits_profile = np.zeros((L, 22), dtype=np.float32)
reweighted_profile = np.zeros((L, 22), dtype=np.float32)
for i in range(L):
for j in range(aln.shape[0]):
hhblits_profile[i, mapping[aln[j, i]]] = 1
reweighted_profile[i, mapping[aln[j, i]]] = seq_weight[j]
hhblits_profile /= hhblits_profile.sum(-1).reshape(-1, 1)
reweighted_profile /= reweighted_profile.sum(-1).reshape(-1, 1)
mapping = {aa: i for i, aa in enumerate('ARNDCQEGHILKMFPSTWYV-')}
non_gapped_profile = np.zeros((L, 21), dtype=np.float32)
for i in range(L):
for j in aln[:, i]:
non_gapped_profile[i, mapping[j]] = 1
non_gapped_profile[:, -1] = 0
non_gapped_profile /= non_gapped_profile.sum(-1).reshape(-1, 1)
mapping = {aa: i for i, aa in enumerate('-ARNDCQEGHILKMFPSTWYVX')}
a2n = np.frompyfunc(lambda x: mapping[x], 1, 1)
fi, fij, Meff = calculate_f(a2n(aln))
MI = calculate_MI(fi, fij)
data = {
'chain_name': target,
'domain_name': name,
'sequence': seq,
'seq_length': np.ones((L, 1), dtype=np.int64)*L,
'residue_index': np.arange(L, dtype=np.int64).reshape(L, 1),
'aatype': sequence_to_onehot(seq),
# profile: A profile (probability distribution over amino acid types)
# computed using PSI-BLAST. Equivalent to the output of ChkParse.
'hhblits_profile': hhblits_profile,
'reweighted_profile': reweighted_profile,
'hmm_profile': extract_hmm_profile(hhm_file.read_text(), seq),
'num_alignments': np.ones((L, 1), dtype=np.int64) * aln.shape[0],
'deletion_probability': np.float32(aln=='-').mean(0).reshape(-1,1),
'gap_matrix': gap_matrix,
'non_gapped_profile': non_gapped_profile,
# plmDCA
'pseudo_frob': pseudo_frob,
'pseudo_bias': pseudo_bias,
'pseudolikelihood': pseudolikelihood,
'num_effective_alignments': np.float32(Meff),
'mutual_information': MI,
# no need features for prediction
'resolution': np.float32(0),
'sec_structure': np.zeros((L, 8), dtype=np.int64),
'sec_structure_mask': np.zeros((L, 1), dtype=np.int64),
'solv_surf': np.zeros((L, 1), dtype=np.float32),
'solv_surf_mask': np.zeros((L, 1), dtype=np.int64),
'alpha_positions': np.zeros((L, 3), dtype=np.float32),
'alpha_mask': np.zeros((L, 1), dtype=np.int64),
'beta_positions': np.zeros((L, 3), dtype=np.float32),
'beta_mask': np.zeros((L, 1), dtype=np.int64),
'superfamily': '',
'between_segment_residues': np.zeros((L, 1), dtype=np.int64),
'phi_angles': np.zeros((L, 1), dtype=np.float32),
'phi_mask': np.zeros((L, 1), dtype=np.int64),
'psi_angles': np.zeros((L, 1), dtype=np.float32),
'psi_mask': np.zeros((L, 1), dtype=np.int64),
# to be fixed soon
'profile': np.zeros((L, 21), dtype=np.float32),
'profile_with_prior': np.zeros((L, 22), dtype=np.float32),
'profile_with_prior_without_gaps': np.zeros((L, 21), dtype=np.float32)
}
dataset.append(data)
np.save(out_file, dataset, allow_pickle=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Alphafold - PyTorch version')
parser.add_argument('-s', '--seq', type=str, required=True, help='target protein fasta file')
parser.add_argument('-o', '--out', type=str, default=None, help='output file')
parser.add_argument('-c', '--crop', default=False, action='store_true', help='make crops')
parser.add_argument('-f', '--feature', default=False, action='store_true', help='make features')
args = parser.parse_args()
SEQ_FILE = Path(args.seq)
if args.crop:
make_crops(SEQ_FILE)
elif args.feature:
OUT_FILE = args.out if args.out is not None else SEQ_FILE.parent / SEQ_FILE.stem
feature_generation(SEQ_FILE, OUT_FILE)