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import numpy as np
import scipy
import scipy.spatial
import string
import os,re
import random
import util
import gzip
to1letter = {
"ALA":'A', "ARG":'R', "ASN":'N', "ASP":'D', "CYS":'C',
"GLN":'Q', "GLU":'E', "GLY":'G', "HIS":'H', "ILE":'I',
"LEU":'L', "LYS":'K', "MET":'M', "PHE":'F', "PRO":'P',
"SER":'S', "THR":'T', "TRP":'W', "TYR":'Y', "VAL":'V' }
# read A3M and convert letters into
# integers in the 0..20 range,
# also keep track of insertions
def parse_a3m(filename):
msa = []
ins = []
table = str.maketrans(dict.fromkeys(string.ascii_lowercase))
#print(filename)
if filename.split('.')[-1] == 'gz':
fp = gzip.open(filename, 'rt')
else:
fp = open(filename, 'r')
# read file line by line
for line in fp:
# skip labels
if line[0] == '>':
continue
# remove right whitespaces
line = line.rstrip()
if len(line) == 0:
continue
# remove lowercase letters and append to MSA
msa.append(line.translate(table))
# sequence length
L = len(msa[-1])
# 0 - match or gap; 1 - insertion
a = np.array([0 if c.isupper() or c=='-' else 1 for c in line])
i = np.zeros((L))
if np.sum(a) > 0:
# positions of insertions
pos = np.where(a==1)[0]
# shift by occurrence
a = pos - np.arange(pos.shape[0])
# position of insertions in cleaned sequence
# and their length
pos,num = np.unique(a, return_counts=True)
# append to the matrix of insetions
i[pos] = num
ins.append(i)
if len(msa) == 10000:
break
# convert letters into numbers
alphabet = np.array(list("ARNDCQEGHILKMFPSTWYV-"), dtype='|S1').view(np.uint8)
msa = np.array([list(s) for s in msa], dtype='|S1').view(np.uint8)
for i in range(alphabet.shape[0]):
msa[msa == alphabet[i]] = i
# treat all unknown characters as gaps
msa[msa > 20] = 20
ins = np.array(ins, dtype=np.uint8)
return msa,ins
# read and extract xyz coords of N,Ca,C atoms
# from a PDB file
def parse_pdb(filename):
lines = open(filename,'r').readlines()
return parse_pdb_lines(lines)
#'''
def parse_pdb_lines(lines):
# indices of residues observed in the structure
idx_s = [int(l[22:26]) for l in lines if l[:4]=="ATOM" and l[12:16].strip()=="CA"]
# 4 BB + up to 10 SC atoms
xyz = np.full((len(idx_s), 14, 3), np.nan, dtype=np.float32)
for l in lines:
if l[:4] != "ATOM":
continue
resNo, atom, aa = int(l[22:26]), l[12:16], l[17:20]
idx = idx_s.index(resNo)
for i_atm, tgtatm in enumerate(util.aa2long[util.aa2num[aa]]):
if tgtatm == atom:
xyz[idx,i_atm,:] = [float(l[30:38]), float(l[38:46]), float(l[46:54])]
break
# save atom mask
mask = np.logical_not(np.isnan(xyz[...,0]))
xyz[np.isnan(xyz[...,0])] = 0.0
return xyz,mask,np.array(idx_s)
#'''
'''
def parse_pdb_lines(lines):
# indices of residues observed in the structure
#idx_s = [int(l[22:26]) for l in lines if l[:4]=="ATOM" and l[12:16].strip()=="CA"]
res = [(l[22:26],l[17:20]) for l in lines if l[:4]=="ATOM" and l[12:16].strip()=="CA"]
idx_s = [int(r[0]) for r in res]
seq = [util.aa2num[r[1]] if r[1] in util.aa2num.keys() else 20 for r in res]
# 4 BB + up to 10 SC atoms
xyz = np.full((len(idx_s), 14, 3), np.nan, dtype=np.float32)
for l in lines:
if l[:4] != "ATOM":
continue
resNo, atom, aa = int(l[22:26]), l[12:16], l[17:20]
idx = idx_s.index(resNo)
for i_atm, tgtatm in enumerate(util.aa2long[util.aa2num[aa]]):
if tgtatm == atom:
xyz[idx,i_atm,:] = [float(l[30:38]), float(l[38:46]), float(l[46:54])]
break
# save atom mask
mask = np.logical_not(np.isnan(xyz[...,0]))
xyz[np.isnan(xyz[...,0])] = 0.0
return xyz,mask,np.array(idx_s), np.array(seq)
'''
def parse_templates(item, params):
# init FFindexDB of templates
### and extract template IDs
### present in the DB
ffdb = FFindexDB(read_index(params['FFDB']+'_pdb.ffindex'),
read_data(params['FFDB']+'_pdb.ffdata'))
#ffids = set([i.name for i in ffdb.index])
# process tabulated hhsearch output to get
# matched positions and positional scores
infile = params['DIR']+'/hhr/'+item[-2:]+'/'+item+'.atab'
hits = []
for l in open(infile, "r").readlines():
if l[0]=='>':
key = l[1:].split()[0]
hits.append([key,[],[]])
elif "score" in l or "dssp" in l:
continue
else:
hi = l.split()[:5]+[0.0,0.0,0.0]
hits[-1][1].append([int(hi[0]),int(hi[1])])
hits[-1][2].append([float(hi[2]),float(hi[3]),float(hi[4])])
# get per-hit statistics from an .hhr file
# (!!! assume that .hhr and .atab have the same hits !!!)
# [Probab, E-value, Score, Aligned_cols,
# Identities, Similarity, Sum_probs, Template_Neff]
lines = open(infile[:-4]+'hhr', "r").readlines()
pos = [i+1 for i,l in enumerate(lines) if l[0]=='>']
for i,posi in enumerate(pos):
hits[i].append([float(s) for s in re.sub('[=%]',' ',lines[posi]).split()[1::2]])
# parse templates from FFDB
for hi in hits:
#if hi[0] not in ffids:
# continue
entry = get_entry_by_name(hi[0], ffdb.index)
if entry == None:
continue
data = read_entry_lines(entry, ffdb.data)
hi += list(parse_pdb_lines(data))
# process hits
counter = 0
xyz,qmap,mask,f0d,f1d,ids = [],[],[],[],[],[]
for data in hits:
if len(data)<7:
continue
qi,ti = np.array(data[1]).T
_,sel1,sel2 = np.intersect1d(ti, data[6], return_indices=True)
ncol = sel1.shape[0]
if ncol < 10:
continue
ids.append(data[0])
f0d.append(data[3])
f1d.append(np.array(data[2])[sel1])
xyz.append(data[4][sel2])
mask.append(data[5][sel2])
qmap.append(np.stack([qi[sel1]-1,[counter]*ncol],axis=-1))
counter += 1
xyz = np.vstack(xyz).astype(np.float32)
mask = np.vstack(mask).astype(np.bool)
qmap = np.vstack(qmap).astype(np.long)
f0d = np.vstack(f0d).astype(np.float32)
f1d = np.vstack(f1d).astype(np.float32)
ids = ids
return xyz,mask,qmap,f0d,f1d,ids
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