-
Notifications
You must be signed in to change notification settings - Fork 5
/
analysis.py
455 lines (367 loc) · 15.2 KB
/
analysis.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
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
import os
import ast
import sys
import math
import heapq
import itertools
import timeit
import itertools
import pickle
from util import *
from collections import OrderedDict
import statium_cpp
def statium(in_res, in_pdb, in_dir, in_ip, out, ip_cutoff_dist, optimize_match, match_cutoffs, backbone, filter_sidechain, counts, verbose):
if verbose: print 'Starting STATUM analysis...'
tic = timeit.default_timer()
sidechain_cpp(in_res, in_pdb, in_dir, in_ip, out, ip_cutoff_dist, optimize_match, match_cutoffs, backbone, filter_sidechain, counts, verbose)
toc = timeit.default_timer()
if verbose: print 'Done in ' + str((toc-tic)/60) + ' minutes! Output in: ' + out
def sidechain_cpp(in_res, in_pdb, in_dir, out, ip_dist_cutoff, optimize_match, match_cutoffs, backbone, filter_sidechains, print_counts, verbose):
#RECEPTOR PEPTIDE
if verbose: print 'Extracting residue position from ' + in_res + '...'
res_lines = filelines2list(in_res)
residues = [int(line.strip())-1 for line in res_lines]
if verbose: print 'Extracting information from ' + in_pdb + '...'
if filter_sidechains: pdbI = get_pdb_info(in_pdb, filter_sidechains = True)
else: pdbI = get_pdb_info(in_pdb)
for res in pdbI:
if res.string_name == 'GLY':
res.correct()
if not backbone:
res.strip_backbone()
pdbSize = len(pdbI)
if verbose: print 'Computing inter-atomic distances and finding interacting pairs...\n'
(distance_matrix, use_indices) = dist_matrix_IPs_2terms(pdbI, residues, ip_dist_cutoff)
if verbose: print use_indices
num_ips = len(use_indices)
energies = {key:0 for key in residues}
for (i, (pos1, pos2)) in enumerate(use_indices):
aa = pdbI[pos1].char_name
dists = filter_sc_dists(pdbI[pos1].atom_names, ['CA', 'CB'], distance_matrix[pos1][pos2-pos1-1], 'forward')
lib_file = os.path.join(in_dir, aa + '.dists')
result = statium_cpp.query_distance(lib_file, str(dists.values()[1:-1], optimize_match, match_cutoffs[aa]))
list_result = ast.literal_eval('[' + result + ']')
for j in range(20): energies[pos2][j] += list_result[j]
with open(out, 'w') as f:
f.write('\t\t' + '\t'.join([AAint2char(i) for i in range(20)]))
for res, energies in energies.iteritems():
f.write(res + '\t' + '\t'.join(energies))
def sidechain_python(in_res, in_pdb, in_pdb_dir, in_ip_dir, out, ip_dist_cutoff, match_dist_cutoffs, backbone, filter_sidechains, print_counts, verbose):
if verbose: print 'Extracting residue position from ' + in_res + '...'
res_lines = filelines2list(in_res)
residues = [int(line.strip())-1 for line in res_lines]
if verbose: print 'Extracting information from ' + in_pdb + '...'
if filter_sidechains: pdbI = get_pdb_info(in_pdb, filter_sidechains = True)
else: pdbI = get_pdb_info(in_pdb)
for res in pdbI:
if res.string_name == 'GLY':
res.correct()
if not backbone:
res.strip_backbone()
pdbSize = len(pdbI)
if verbose: print 'Computing inter-atomic distances and finding interacting pairs...\n'
(distance_matrix, use_indices) = get_dist_matrix_and_IPs_peptide(pdbI, residues, ip_dist_cutoff)
if verbose: print use_indices
num_ips = len(use_indices)
if verbose: print 'Storing distance information for each interacting pair...'
distances = dict()
for (p1,p2) in use_indices:
if pdbI[p2].stubIntact:
p2_base_chain = ['CA','CB']
distances[(p1,p2)] = filter_sc_dists(pdbI[p1].atom_names, p2_base_chain, distance_matrix[p1][p2-p1-1], 'forward')
else:
print 'Position %d does not have a valid stub' % p2
sys.exit(1)
#stores total number of each residue identity across IP
totals = [0 for i in xrange(20)]
#stores total number of 'matching sidechain'
counts = [[0 for j in xrange(20)] for i in xrange(num_ips)]
lib_pdb_paths = [os.path.join(in_pdb_dir, pdb) for pdb in os.listdir(in_pdb_dir)]
for i, lib_pdb_path in enumerate(lib_pdb_paths):
if verbose: print 'Processing ' + lib_pdb_path + ' (' + str(i) + ' of ' + str(len(lib_pdb_paths)) + ')'
lib_pdb = get_pdb_info(lib_pdb_path)
ip_path = os.path.join(in_ip_dir, os.path.split(lib_pdb_path)[1].split('.')[0] + '.ip')
lib_ips = [(int(pair[0]),int(pair[1])) for pair in filelines2deeplist(ip_path) if pair != []]
lib_distance_matrix = get_lib_dist_matrix(lib_pdb, lib_ips)
if not lib_distance_matrix:
print lib_pdb_path
continue
for (lib_pos1, lib_pos2) in lib_ips:
if abs(lib_pos2 - lib_pos1) <= 4: continue
(lib_AA1, lib_AA2) = (lib_pdb[lib_pos1].int_name, lib_pdb[lib_pos2].int_name)
if lib_AA1 < 0 or lib_AA2 < 0 or lib_AA1 > 19 or lib_AA2 > 19: continue
totals[lib_AA1] += 1
totals[lib_AA2] += 1
for (j, (pos1, pos2)) in enumerate(use_indices):
AA1 = pdbI[pos1].int_name
if lib_pdb[lib_pos2].stubIntact:
if lib_AA1==AA1:
p2_base_chain = ['CA', 'CB']
lib_dist = filter_sc_dists(lib_pdb[lib_pos1].atom_names, p2_base_chain, lib_distance_matrix[lib_pos1][lib_pos2-lib_pos1-1], True)
if matching_sidechain_pair(distances[(pos1,pos2)], lib_dist, match_dist_cutoffs[pdbI[pos1].char_name]):
counts[j][lib_AA2] += 1
if lib_pdb[lib_pos1].stubIntact:
if lib_AA2==AA1:
p1_base_chain = ['CA', 'CB']
lib_dist = filter_sc_dists(p1_base_chain, lib_pdb[lib_pos2].atom_names, lib_distance_matrix[lib_pos1][lib_pos2-lib_pos1-1], False)
if matching_sidechain_pair(distances[(pos1,pos2)], lib_dist, match_dist_cutoffs[pdbI[pos1].char_name]):
counts[j][lib_AA1] += 1
if(verbose): print('Finished processing library .pdb files.')
if print_counts:
if verbose: print 'Writing raw library counts to files...'
counts_dir = out + '_counts'
if not os.path.exists(counts_dir):
os.mkdir(counts_dir)
total_counts_file = open(os.path.join(counts_dir, 'lib_ip_residue_totals.txt'), 'w')
for i in xrange(20): total_counts_file.write(AAint2char(i) + '\t' + str(totals[i]) + '\n')
total_counts_file.close()
for (i, pair) in enumerate(use_indices):
counts_file = open(os.path.join(counts_dir, str(pair[0] + 1) + '_' + str(pair[1] + 1) + '_counts.txt'), 'w')
for j in xrange(20): counts_file.write(AAint2char(j) + '\t' + str(counts[i][j]) + '\n')
counts_file.close()
if verbose: print("Computing probabilities from counts...")
probs = determine_probs(use_indices, totals, counts)
if len(use_indices) != len(probs):
print 'Number of IPs is not consistent'
print use_indices
print probs
sys.exit(1)
write_output(use_indices, probs, out)
if(verbose): print("Finished calculating probabilities. Written to: " + out + '_probs')
def get_dist_matrix_and_IPs_peptide(pdb, residues, cutoff):
N = len(pdb)
distance_matrix = [[None]*(N-i-1) for i in xrange(N)]
ips = set()
first = True
print '\tOut of %d residues finished:' % N
for i in xrange(N):
for j in xrange(i+1, N):
if (i in residues) ^ (j in residues):
result = pdb[i].fastFilteredDistancesTo(pdb[j], cutoff)
distance_matrix[i][j-i-1] = result
if result is not None:
if i in residues:
ips.add((j,i))
else:
ips.add((i,j))
return (distance_matrix, ips)
def dist_matrix_IPs_2terms(pdb, residues, cutoff):
N = len(pdb)
distance_matrix = [[None]*(N-i-1) for i in xrange(N)]
ips = set()
first = True
print '\tOut of %d residues finished:' % N
for i in xrange(N):
for j in xrange(i+1, N):
if j-i <=4: continue
result = pdb[i].fastFilteredDistancesTo(pdb[j], cutoff)
distance_matrix[i][j-i-1] = result
if result is not None:
if i in residues:
ips.add((j,i))
else:
ips.add((i,j))
return (distance_matrix, ips)
def get_lib_dist_matrix(pdb, ips):
N = len(pdb)
matrix = [[None]*(N-i-1) for i in xrange(N)]
for (i,j) in ips:
try:
matrix[i][j-i-1] = pdb[i].fastDistancesTo(pdb[j])
except IndexError:
print 'Invalid indices'
print i,j
print len(matrix), len(matrix[0])
print len(pdb)
return None
return matrix
def write_output(use_indices, probs, out):
AAs = [AAint2char(i) for i in xrange(20)]
with open(out, 'w') as f:
s = 'IPs\t' + '\t'.join(AAs)
f.write(s + '\n')
for l, (i,j) in enumerate(use_indices):
s = str(i) + '-' + str(j)
s2 = '\t'.join([str(probs[l][aa]) for aa in AAs])
f.write(s + '\t' + s2 + '\n')
def read_output(in_path):
lines = filelines2deeplist(in_path, skipComments=True, skipEmptyLines=True)
AAs = [AAint2char(i) for i in xrange(20)]
probs = OrderedDict()
for ip_line in lines[1:]:
ip = tuple(ip_line[0].split('-'))
use_indices.append(ip)
aa_probs = dict()
for i, prob in enumerate(ip_line[1:]):
aa_probs[AAs[i]] = prob
probs[ip] = aa_probs
return probs
def determine_probs(use_indices, totals, counts):
'''Converts library counts to energies
Takes in interacting pairs, total IP counts (list of length 20), and AA count frequences for each peptide IP
Energies are output as list of dicts (key:value as AA character:energy value), one dict per IP'''
#total number of residues across all library interacting pairs
lib_sum = float(sum(totals))
#frequency of each residue in total library
lib_total_probs = [x/lib_sum for x in totals]
out = list()
for (i, pair) in enumerate(use_indices):
total = float(sum(counts[i]))
if total > 99:
probs = dict()
#probability of each residue occuring at *that* IP (i.e. / by total res's at the IP)
AA_probs = [(x/total if x != 0 else 1/total) for x in counts[i]]
for j in xrange(20):
e = -1.0 * math.log(AA_probs[j] / lib_total_probs[j])
probs[AAint2char(j)] = e
out.append(probs)
return out
#Assumes fast & distances stored as dict (atom1_name, atom2_name): distance val
def matching_sidechain_pair(dists1, dists2, cutoff):
sd = 0.0
count = 0.0
for pair1, dist1 in dists1.iteritems():
for pair2, dist2 in dists2.iteritems():
if pair1 == pair2:
sd += ((dist1 - dist2) ** 2)
count += 1.0
if math.sqrt(sd / count) < cutoff: return True
else: return False
#returns a more sparse distance matrix, filled on at IP positions
# uses the library pdb_info
def get_distance_matrix_ip(pdb, ips):
N = len(pdb)
distance_matrix = [[None]*N for j in xrange(N)]
for (i,j) in ips:
try:
distance_matrix[i][j] = pdb[i].distancesTo(pdb[j])
except:
print len(pdb)
print pdb
print i,j,N
return distance_matrix
#If fast, assumes pos1_list is atom_names (not atom objects), and pair_dists is indexed the same way
def filter_sc_dists(pos1_list, pos2_list, pair_dists, forward, output_dict=True, fast=True):
if output_dict:
out = dict()
if forward:
for atomi in pos1_list:
for atomj in pos2_list:
out[(atomi, atomj)] = pair_dists[(atomi, atomj)]
else:
for atomj in pos2_list:
for atomi in pos1_list:
out[(atomj, atomi)] = pair_dists[(atomi, atomj)]
return out
else:
pairs = list()
dists = list()
if forward:
for atomi in pos1_list:
for atomj in pos2_list:
idx = distance_matrix[0].index((atomi, atomj))
pairs.append((atomi, atomj))
dists.append(distance_matrix[1][idx])
else:
for atomj in pos2_list:
for atomi in pos1_list:
idx = distance_matrix[0].index((atomi, atomj))
pairs.append((atomj, atomi))
dists.append(distance_matrix[1][idx])
return [pairs, dists]
def generate_random_distribution (in_res, in_probs_dir, num_seqs=1000):
sequence_length = len(filelines2list(in_res))
print 'Calculating distribution of energies for %d random sequences...', num_seqs
energies = list()
for i in xrange(num_seqs):
if i % 100 is 0:
print i,
sys.stdout.flush()
energies.append(calc_seq_energy(in_res, in_probs_dir, generate_random_seq(sequence_length)))
energies.sort()
avg = mean(energies)
sd = std(energies)
return (sequence_length, energies, avg, sd)
def calc_seq_energy (in_res_path, in_probs, seq):
#Handle two cases: e.g. AAAX,LLL and LXAAM
seq = ''.join(seq.split(','))
#loading in probability into all_probs
all_probs = read_output(in_probs)
energy = 0
lines = filelines2list(in_res_path)
residues = [int(line.strip()) for line in lines]
for i, residue in enumerate(residues):
filtered = {ip: probs for ip, probs in all_probs.items() if ip[1] == residue}
AA = seq[i-1]
if AA == 'X' or AA == 'G' or filtered == {}: continue
for ip, probs in filtered.items():
energy += probs[AAchar2int(AA)]
return energy
#Note: need res file, because not all residues
def calc_top_seqs(in_res_path, in_probs, num_sequences):
#read back from .res file where ligand residues start
lines = filelines2list(in_res_path)
residues = [int(line.strip()) for line in lines]
#loading in probability into all_probs
prob_files = os.listdir(probs_dir)
all_probs = read_output(in_probs)
#the following now fills ordered_probs
#[[sorted list of AA probs for a residue position: (0.5, 'A'), (0.3, 'C'),...], [like before for residue pos 2], etc...]
ordered_probs = []
AAs = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
for residue in residues:
probs = [prob for ip, prob in all_probs.items() if residue in ip]
probs_sum = map(sum, zip(*probs))
sorted_probs = sorted(zip(probs_sum, AAs), key=lambda pair: pair[0])
ordered_probs.append(sorted_probs)
#enumerate all the balls in urns possibilities
#note that to maintain the max-heap property in Python's heapq implementation (which only does min-heap), we multiple by -1 before adding to heap
num_urns = len(residues)
heap = []
seq = ''
energy = 0
for i, c in enumerate(residues):
if ordered_probs[i]:
aa = ordered_probs[i][0][1]
seq += aa
energy += (0.0 if aa == 'G' else ordered_probs[i][0][0])
else:
seq += 'X'
heapq.heappush(heap, (energy, seq))
max_num_balls = 0
total = 0
while total < num_sequences:
max_num_balls += 1
total += nCr((max_num_balls+num_urns-1), (max_num_balls))
for num_balls in xrange(max_num_balls+1)[1:]:
combo_elements = xrange(num_balls+num_urns-1)
combos = list(itertools.combinations(combo_elements, (num_urns-1)))
for combo in combos:
#print combo
urn_counts = [0]*num_urns
for i, position in enumerate(combo):
if(i == 0):
urn_counts[i] = position
elif(i == len(combo)-1):
urn_counts[i] = position - combo[i-1] - 1
urn_counts[i+1] = len(combo_elements)-1-position
else:
urn_counts[i] = position - combo[i-1] - 1
seq = ''
energy = 0
for i, c in enumerate(urn_counts):
if(ordered_probs[i]):
aa = ordered_probs[i][c][1]
seq += aa
energy += (0.0 if aa == 'G' else ordered_probs[i][c][0])
else:
seq += 'X'
if(seq in [i[1] for i in heap]):
continue
if(len(heap) < num_sequences):
heapq.heappush(heap, (-1*energy, seq))
elif(energy < heap[0][0]*-1):
heapq.heappushpop(heap, (-1*energy, seq))
heap = sorted(heap, reverse=True)
out = [(seq, energy*-1) for energy, seq in heap]
return out