def get_ppv_hbond(fasta_filename,
c_filename,
hbond_filename,
factor=1.0,
min_score=-1.0,
sep=' ',
outfilename=''):
acc = fasta_filename.split('.')[-2][-5:-1]
### get sequence
seq = parse_fasta.read_fasta(open(fasta_filename, 'r')).values()[0][0]
ref_len = len(seq)
### get top "factor" * "ref_len" predicted contacts
contacts = parse_contacts.parse(open(c_filename, 'r'), sep)
contacts_x = []
contacts_y = []
scores = []
contact_dict = {}
count = 0
for i in range(len(contacts)):
score = contacts[i][0]
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
pos_diff = abs(c_x - c_y)
too_close = pos_diff < 5
if not too_close:
contacts_x.append(c_x)
contacts_y.append(c_y)
scores.append(score)
count += 1
if min_score == -1.0 and count >= ref_len * factor:
break
if score < min_score:
break
ref_contact_map = np.zeros((ref_len, ref_len))
hbonds_raw = open(hbond_filename).readlines()
hbonds = [line.strip().split(' ')
for line in hbonds_raw] #map(split(' '), map(strip, hbonds_raw))
for h in hbonds:
i = int(h[0]) - 1
j = int(h[1]) - 1
val = float(h[2])
ref_contact_map[i, j] = -val
ref_contact_map[j, i] = -val
PPV, TP, FP = get_ppv_helper(contacts_x, contacts_y, ref_contact_map,
ref_len, factor)
print '%s %s %s %s' % (hbond_filename, PPV, TP, FP)
return (hbond_filename, PPV, TP, FP)
def realign(fasta_filename, pdb_filename, outfilename='', chain='*'):
### get sequence
seq = list(parse_fasta.read_fasta(open(fasta_filename,
'r')).values())[0][0]
ref_len = len(seq)
atom_seq = parse_pdb.get_atom_seq(open(pdb_filename, 'r'), chain)
pdbfile = open(pdb_filename, 'r')
align = pairwise2.align.globalms(atom_seq, seq, 2, -1, -0.5, -0.1)
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
#print (atom_seq_ali,seq_ali)
res_i = -9999
resno = {}
i = 0
atompos = 0
seqpos = 0
maxlen = len(atom_seq_ali)
for i in range(0, maxlen):
if atom_seq_ali[i] == "-":
seqpos += 1
elif seq_ali[i] == "-":
atompos += 1
resno[atompos] = -9999
else:
atompos += 1
seqpos += 1
resno[atompos] = seqpos
if not chain:
chain = get_first_chain(pdbfile)
pdbfile.seek(0)
i = 0
for line in pdbfile:
if not line.startswith('ATOM'):
continue
atm_record = parse_pdb.parse_atm_record(line)
if atm_record['chain'] != ' ' and atm_record[
'chain'] != chain and chain != '*':
continue
if atm_record['res_no'] != res_i:
i += 1
res_i = atm_record['res_no']
atm_record['res_no'] = resno[i]
#print (atm_record)
if resno[i] > 0:
parse_pdb.write_pdb_atm_record(atm_record)
#res_dict[res_i].append(np.array(atm))
#pdbfile.close()
return
def plot_map(fasta_filename,
c_filename,
factor,
c2_filename='',
ss_fname='',
psipred_horiz_fname='',
psipred_vert_fname='',
pdb_filename='',
is_heavy=False,
chain='',
sep=',',
outfilename=''):
acc = fasta_filename.split('.')[0][:4]
# get sequence
seq = list(parse_fasta.read_fasta(open(fasta_filename,
'r')).values())[0][0]
ref_len = len(seq)
# get top "factor" * "ref_len" predicted contacts
contacts = parse_contacts.parse(open(c_filename, 'r'), sep)
contacts_x = []
contacts_y = []
scores = []
# contact_dict = {}
count = 0
for i in range(len(contacts)):
score = contacts[i][0]
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
pos_diff = abs(c_x - c_y)
too_close = pos_diff < 5 # Also checking here,
# should remove in parse
# too_close = False
if not too_close:
contacts_x.append(c_x)
contacts_y.append(c_y)
scores.append(score)
count += 1
if count >= ref_len * factor:
break
# start plotting
fig = plt.figure()
ax = fig.add_subplot(111)
# plot secondary structure on the diagonal if given
if psipred_horiz_fname or psipred_vert_fname or ss_fname:
if psipred_horiz_fname:
ss = parse_psipred.horizontal(open(psipred_horiz_fname, 'r'))
elif psipred_vert_fname:
ss = parse_psipred.vertical(open(psipred_vert_fname, 'r'))
else:
ss = parse_ss.parse(open(ss_fname, 'r'))
assert len(ss) == ref_len
for i in range(len(ss)):
if ss[i] == 'H':
plt.plot(i, i, 'o', c='#8B0043', mec="#8B0043", markersize=2)
if ss[i] == 'E':
plt.plot(i, i, 'D', c='#0080AD', mec="#0080AD", markersize=2)
if ss[i] == 'C':
continue
# plot reference contacts in the background if given
if pdb_filename:
res_lst = parse_pdb.get_coordinates(open(pdb_filename, 'r'), chain)
cb_lst = parse_pdb.get_cb_coordinates(open(pdb_filename, 'r'), chain)
atom_seq = parse_pdb.get_atom_seq(open(pdb_filename, 'r'), chain)
align = pairwise2.align.globalms(atom_seq, seq, 2, -1, -0.5, -0.1)
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
j = 0
gapped_res_lst = []
gapped_cb_lst = []
for i in range(len(atom_seq_ali)):
if atom_seq_ali[i] == '-':
gapped_res_lst.append('-')
gapped_cb_lst.append('-')
elif seq_ali[i] == '-':
j += 1
continue
else:
gapped_res_lst.append(res_lst[j])
gapped_cb_lst.append(cb_lst[j])
j += 1
if is_heavy:
dist_mat = get_heavy_contacts(gapped_res_lst)
heavy_cutoff = 5
ref_contact_map = dist_mat < heavy_cutoff
ref_contacts = np.where(dist_mat < heavy_cutoff)
else:
dist_mat = get_cb_contacts(gapped_cb_lst)
cb_cutoff = 8
ref_contact_map = dist_mat < cb_cutoff
ref_contacts = np.where(dist_mat < cb_cutoff)
ref_contacts_x = ref_contacts[0]
ref_contacts_y = ref_contacts[1]
PPVs, TPs, FPs = get_ppvs(contacts_x, contacts_y, ref_contact_map,
atom_seq_ali, ref_len, factor)
tp_colors = get_tp_colors(contacts_x, contacts_y, ref_contact_map,
atom_seq_ali)
print('%s %s %s %s' % (pdb_filename, PPVs[-1], TPs[-1], FPs[-1]))
ax.scatter(ref_contacts_x,
ref_contacts_y,
marker='o',
c='#CCCCCC',
lw=0,
edgecolor='#CCCCCC')
# plot predicted contacts from second contact map if given
if c2_filename:
contacts2 = parse_contacts.parse(open(c2_filename, 'r'), sep)
contacts2_x = []
contacts2_y = []
scores2 = []
# contact_dict2 = {}
count = 0
for i in range(len(contacts2)):
score = contacts2[i][0]
c_x = contacts2[i][1] - 1
c_y = contacts2[i][2] - 1
pos_diff = abs(c_x - c_y)
too_close = pos_diff < 5
if not too_close:
contacts2_x.append(c_x)
contacts2_y.append(c_y)
scores2.append(score)
count += 1
if count >= ref_len * factor:
break
# use TP/FP color coding if reference contacts given
if pdb_filename:
PPVs2, TPs2, FPs2 = get_ppvs(contacts2_x, contacts2_y,
ref_contact_map, atom_seq_ali,
ref_len, factor)
tp2_colors = get_tp_colors(contacts2_x, contacts2_y,
ref_contact_map, atom_seq_ali)
print('%s %s %s %s' %
(pdb_filename, PPVs2[-1], TPs2[-1], FPs2[-1]))
fig.suptitle('%s\nPPV (upper left) = %.2f |' % (PPVs[-1]) +
'PPV (lower right) = %.2f' % (PPVs2[-1]))
sc = ax.scatter(contacts2_y[::-1],
contacts2_x[::-1],
marker='o',
c=tp2_colors[::-1],
s=6,
alpha=0.75,
linewidths=0.0)
sc = ax.scatter(contacts_x[::-1],
contacts_y[::-1],
marker='o',
c=tp_colors[::-1],
s=6,
alpha=0.75,
linewidths=0.0)
else:
sc = ax.scatter(contacts2_y[::-1],
contacts2_x[::-1],
marker='o',
c='#D70909',
edgecolor='#D70909',
s=4,
linewidths=0.5)
sc = ax.scatter(contacts_x[::-1],
contacts_y[::-1],
marker='o',
c='#004F9D',
edgecolor='#004F9D',
s=4,
linewidths=0.5)
# plot predicted contacts from first contact map on both triangles
# if no second contact map given
else:
if pdb_filename:
fig.suptitle('%s\nPPV = %.2f' % (acc, PPVs[-1]))
sc = ax.scatter(contacts_x[::-1],
contacts_y[::-1],
marker='o',
c=tp_colors[::-1],
s=6,
alpha=0.75,
linewidths=0.0)
sc = ax.scatter(contacts_y[::-1],
contacts_x[::-1],
marker='o',
c=tp_colors[::-1],
s=6,
alpha=0.75,
linewidths=0.0)
else:
sc = ax.scatter(contacts_x[::-1],
contacts_y[::-1],
marker='o',
c=scores[::-1],
s=4,
alpha=0.75,
cmap=cm.jet,
linewidths=0.1)
sc = ax.scatter(contacts_y[::-1],
contacts_x[::-1],
marker='o',
c=scores[::-1],
s=4,
alpha=0.75,
cmap=cm.jet,
linewidths=0.1)
plt.colorbar(sc)
plt.gca().set_xlim([0, ref_len])
plt.gca().set_ylim([0, ref_len])
if outfilename:
if outfilename.endswith('.pdf'):
pp = PdfPages(outfilename)
pp.savefig(fig)
pp.close()
elif outfilename.endswith(('.png', '.jpg', '.jpeg')):
plt.savefig(outfilename)
else:
pp = PdfPages('%s.pdf' % outfilename)
pp.savefig(fig)
pp.close()
else:
pp = PdfPages('%s_ContactMap.pdf' % c_filename)
pp.savefig(fig)
pp.close()
plt.show()
def get_ppv(
fasta_filename,
contact_filename,
pdb_filename,
factor_value,
cb_cutoff,
min_score,
chain1,
chain2,
outfilename,
name,
noalign,
min_dist,
print_dist
):
""" Return a tupla of 1 str and 3 floats,
(pdb_filename, PPV, TP, FP). """
# From a dictionary builded from a file, could be a simple fasta,
# a3m, or a MSA, get first seq. The dictionary has this structure:
# {header:[seq1,seq2,...,seqN], header2:[seq1,seq2,...,seqN]} <--- Is it correct?
# The KEYs are the query IDs.
print("In get_ppv function")
seq = list(parse_fasta.read_fasta(fasta_filename).values())[0][0]
ref_len = len(seq)
# Get all the scores from contacts that satisfies the arguments
contacts_x, contacts_y, scores = get_scores_from_contacts(contact_filename, min_dist, factor_value, min_score, ref_len)
# Create a carbon-beta list from chain1 like this:
# [array_cb1([x1, y1, z1]), array_cb2([x2, y2, z2], ... array_cbN([xN, yN, zN])
cb_chain1_lst = parse_pdb.get_cb_coordinates(pdb_filename, chain1)
##########################################
# Using Biopython to get the coordinates #
##########################################
# from Bio.PDB.PDBParser import PDBParser
# parser = PDBParser(PERMISSIVE=1) # See 11.7 Common problems in PDB files --> http://biopython.org/DIST/docs/tutorial/Tutorial.html#htoc150
# structure_id = "1bih"
# filename = "<PATH-TO-PDB-FILE>XXXX.pdb"
# structure = parser.get_structure(structure_id, filename)
#
# for model in structure.get_list():
# for chain in model.get_list():
# if chain == model["B"]:
# for residue in chain.get_list():
# if residue.has_id("CB"):
# cb = residue["CB"]
# print(cb.get_coord())
#
#[46.114 29.797 48.287]
# Here "noalign" is always setting FALSE in function definition. Why?
if noalign:
dist_mat = get_cb_contacts(cb_chain1_lst)
# cb_cutoff = 8
# Check if those are less than "cb_cutoff" angstrom far from each other.
ref_contact_map = dist_mat < cb_cutoff # cb_cutoff is 8 in default mode
# Get all the PPV, TP and FP results.
PPV, TP, FP = get_ppv_helper(
contacts_x, contacts_y, ref_contact_map, atom_seq_ali=[]
)
else:
# Check if PPI is need it
if (chain2 != "" and chain2 != chain1):
print("In the PPI branch of get_ppv")
# Create a carbon-beta list from chain2
cb_chain2_lst = parse_pdb.get_cb_coordinates(pdb_filename, chain2)
gapped_cb_chain1_lst = get_gapped_cb_lts(pdb_filename, chain1, seq, cb_chain1_lst)
gapped_cb_chain2_lst = get_gapped_cb_lts(pdb_filename, chain2, seq, cb_chain2_lst)
# Get the distance matrix from chain1 only.
# I do not use this distance matrix but could
# be useful if intra-chain is also need it to be printed.
# dist_mat_chain1 = get_cb_contacts(gapped_cb_chain1_lst)
# Get the distance matrix from chain1 vs chain2. Used in PPI
dist_mat_chain1_vs_chain2 = get_cb_contacts_PPI(gapped_cb_chain1_lst, gapped_cb_chain2_lst)
# cb_cutoff = 8
# Check if those are less than "cb_cutoff" angstrom far from each other.
# This create a boolean matrix called: ref_contact_map
# cb_cutoff is 8 in default mode
ref_contact_map = dist_mat_chain1_vs_chain2 < cb_cutoff
# Get atoms seq aligned from a PDB_chain.
# atom_seq_ali it is a string.
atom_seq_ali_chain1 = get_global_align_from_pdb(pdb_filename, chain1, seq)[-1][0]
atom_seq_ali_chain2 = get_global_align_from_pdb(pdb_filename, chain2, seq)[-1][0]
###################################################
## Which atom_seq_ali_chainX we have to use for ##
## get_ppf_helper() ? Both? or that one with ## <----- LOOK !
## the best aligned pdb sequence to fasta_seq ? ##
###################################################
print("Getting PPV, TP and FP values...")
# Get all the PPV, TP and FP results.
PPV, TP, FP = get_ppv_helper(
contacts_x,
contacts_y,
ref_contact_map,
atom_seq_ali_chain1
)
# Check if print is need it.
if print_dist:
print("Printing PPI's distance results...")
print_distances_PPI(
contacts_x,
contacts_y,
scores,
dist_mat_chain1_vs_chain2,
atom_seq_ali_chain1,
atom_seq_ali_chain2,
outfilename
)
else:
print("In the monomer branch of get_ppv")
gapped_cb_chain1_lst = get_gapped_cb_lts(pdb_filename, chain1, seq, cb_chain1_lst)
# Get the distance matrix from chain1 only. Could be useful if
# intra-chain is also need it to be printed.
dist_mat = get_cb_contacts(gapped_cb_chain1_lst)
# cb_cutoff = 8
# Check if those are less than "cb_cutoff" angstrom far from each other.
# This create a boolean matrix called: ref_contact_map
# cb_cutoff is 8 in default mode.
ref_contact_map = dist_mat < cb_cutoff
# Get atoms seq aligned from a PDB_chain.
# atom_seq_ali it is a string.
atom_seq_ali_chain1 = get_global_align_from_pdb(pdb_filename, chain1, seq)[-1][0]
# Get the PPV, TP and FP results.
PPV, TP, FP = get_ppv_helper(
contacts_x,
contacts_y,
ref_contact_map,
atom_seq_ali_chain1
)
# Check if print is need it.
if print_dist:
print_distances(
contacts_x,
contacts_y,
scores,
dist_mat,
atom_seq_ali_chain1,
outfilename
)
# Here "name" is always empty (so False) by default.
if name:
print("%s\n" % ("----------------------------------"))
print("%s %s %s %s" % (name, PPV, TP, FP))
else:
print("Finished")
# print("%s %s %s %s %s" % (fasta_filename, contact_filename, PPV, TP, FP))
return (pdb_filename, PPV, TP, FP)
#!/usr/bin/env python
import sys
sys.path.append("/home/x_arnel/git/bioinfo-toolbox/")
from parsing import parse_fasta
from parsing import parse_contacts
sfile = sys.argv[1]
cfile = sys.argv[2]
target = sys.argv[3]
server = sys.argv[4]
ofilepath = sys.argv[5]
minsep = sys.argv[6]
minscore = sys.argv[7]
seq = parse_fasta.read_fasta(open(sfile)).items()[0][1][0]
contacts = parse_contacts.parse(open(cfile), min_dist=0)
print len(contacts)
print contacts[0]
print seq
ofile = open(ofilepath, 'w')
if server == "Pcons-net":
ofile.write(
"PFRMAT RR\nTARGET %s\nAUTHOR 5450-4562-0389\nMETHOD Pcons-net\nREMARK PconsC3\nMETHOD Improved contact predictions on\nMETHOD small protein families.\nMODEL 1\n"
% target)
elif server == "PconsC2":
ofile.write(
import sys
import os
import shutil
import errno
import stat
from parsing import parse_fasta
if __name__ == '__main__':
infile = open(sys.argv[1], 'r')
seq_dict = parse_fasta.read_fasta(infile)
for header, seq_lst in seq_dict.iteritems():
acc = header.split()[0]
outfile = open(acc + '.fa', 'w')
seq = seq_lst[0]
fasta_string = '>%s\n%s\n' % (header, seq)
#print(fasta_string)
outfile.write(fasta_string)
outfile.close()
infile.close()
def get_ppv(fasta_filename, c_filename, pdb_filename, factor=1.0,
min_score=-1.0, chain='', sep=' ', outfilename='', name='', noalign=False, min_dist=5, print_dist=False):
acc = fasta_filename.split('.')[-2][-5:-1]
### get sequence
seq = parse_fasta.read_fasta(open(fasta_filename, 'r')).values()[0][0]
ref_len = len(seq)
### get top ranked predicted contacts
contacts = parse_contacts.parse(open(c_filename, 'r'), sep, min_dist=min_dist)
contacts_x = []
contacts_y = []
scores = []
contact_dict = {}
count = 0
for i in range(len(contacts)):
score = contacts[i][0]
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
pos_diff = abs(c_x - c_y)
too_close = pos_diff < min_dist
if not too_close:
contacts_x.append(c_x)
contacts_y.append(c_y)
scores.append(score)
count += 1
if min_score == -1.0 and count >= ref_len * factor:
break
if score < min_score:
break
assert(len(contacts_x) == len(contacts_y) == len(scores))
cb_lst = parse_pdb.get_cb_coordinates(open(pdb_filename, 'r'), chain)
bfactor = parse_pdb.get_area(open(pdb_filename, 'r'), chain)
surf = parse_pdb.get_dist_to_surface(open(pdb_filename, 'r'), chain)
if noalign:
dist_mat = get_cb_contacts(cb_lst)
cb_cutoff = 8
ref_contact_map = dist_mat < cb_cutoff
PPV, TP, FP = get_ppv_helper(contacts_x, contacts_y, ref_contact_map, ref_len, factor)
else:
atom_seq = parse_pdb.get_atom_seq(open(pdb_filename, 'r'), chain)
align = pairwise2.align.globalms(atom_seq, seq, 2, -1, -0.5, -0.1)
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
gapped_cb_lst = []
ali_lst =[]
j = 0
k = 0
for i in xrange(len(atom_seq_ali)):
#print i,j,k,seq_ali[i],atom_seq_ali[i]
if atom_seq_ali[i] == '-':
gapped_cb_lst.append(['-'])
ali_lst.append(-9999)
k += 1
elif seq_ali[i] == '-':
j += 1
continue
else:
ali_lst.append(j)
gapped_cb_lst.append(cb_lst[j])
k += 1
j += 1
dist_mat = get_cb_contacts(gapped_cb_lst)
area = parse_pdb.get_area(open(pdb_filename, 'r'), chain)
surf = parse_pdb.get_dist_to_surface(open(pdb_filename, 'r'), chain)
if print_dist:
print_distances(contacts_x, contacts_y, scores, dist_mat,
area, surf, ref_len,ref_len,
seq, ali_lst=ali_lst, atom_seq=atom_seq,
outfile=outfilename)
cb_cutoff = 8
ref_contact_map = dist_mat < cb_cutoff
PPV, TP, FP = get_ppv_helper(contacts_x, contacts_y, ref_contact_map, ref_len, factor, atom_seq_ali=atom_seq_ali)
if name:
print '%s %s %s %s' % (name, PPV, TP, FP)
else:
print '%s %s %s %s %s' % (fasta_filename, c_filename, PPV, TP, FP)
return (pdb_filename, PPV, TP, FP)
def get_ppv(fasta_filenameA,
c_filename,
pdb_filenameA,
fasta_filenameB,
pdb_filenameB,
factor=1.0,
min_score=-1.0,
chainA='',
chainB='',
sep=' ',
outfilename='',
name='',
noalign=False,
min_dist=5,
interfacelen=10,
print_dist=False,
cutoff=0.25):
### get sequence
seqA = parse_fasta.read_fasta(open(fasta_filenameA, 'r')).values()[0][0]
seqB = parse_fasta.read_fasta(open(fasta_filenameB, 'r')).values()[0][0]
seq = seqA + seqA # Actually the contact map sequence is just two copies of seqA
ref_lenA = len(seqA)
ref_lenB = len(seqB)
ref_len = len(seq)
### get top ranked predicted contacts
contacts = parse_contacts.parse(open(c_filename, 'r'),
sep,
min_dist=min_dist)
contacts_x = []
contacts_y = []
scores = []
contactsA_x = []
contactsA_y = []
scoresA = []
contactsB_x = []
contactsB_y = []
scoresB = []
contactsI_x = []
contactsI_y = []
scoresI = []
contact_dict = {}
count = 0
countA = 0
countB = 0
countI = 0
for i in range(len(contacts)):
score = contacts[i][0]
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
#print i,c_x,c_y,score
pos_diff = abs(c_x - c_y)
too_close = pos_diff < min_dist
if not too_close:
# The contacts only covers
contacts_x.append(c_x)
contacts_y.append(c_y)
scores.append(score)
#contacts_x.append(c_x+ref_lenA)
#contacts_y.append(c_y+ref_lenA)
#scores.append(score)
contactsA_x.append(c_x)
contactsA_y.append(c_y)
scoresA.append(score)
contactsB_x.append(c_x)
contactsB_y.append(c_y)
scoresB.append(score)
# if min_score == -1.0 and count >= ref_len * factor:
# break
# if score < min_score:
# break
assert (len(contacts_x) == len(contacts_y) == len(scores))
assert (len(contactsA_x) == len(contactsA_y) == len(scoresA))
assert (len(contactsB_x) == len(contactsB_y) == len(scoresB))
assert (len(contactsI_x) == len(contactsI_y) == len(scoresI))
cb_lstA = parse_pdb.get_cb_coordinates(open(pdb_filenameA, 'r'), chainA)
cb_lstB = parse_pdb.get_cb_coordinates(open(pdb_filenameB, 'r'), chainB)
cb_lst = cb_lstA + cb_lstB
bfactorA = parse_pdb.get_area(open(pdb_filenameA, 'r'), chainA)
bfactorB = parse_pdb.get_area(open(pdb_filenameB, 'r'), chainB)
bfactor = bfactorA + bfactorB
surfA = parse_pdb.get_dist_to_surface(open(pdb_filenameA, 'r'), chainA)
surfB = parse_pdb.get_dist_to_surface(open(pdb_filenameB, 'r'), chainB)
surf = surfA + surfB
#print cb_lst,noalign
if noalign:
dist_mat = get_cb_contacts(cb_lst)
dist_matA = get_cb_contacts(cb_lstA)
dist_matB = get_cb_contacts(cb_lstB)
#PPV, TP, FP = get_ppv_helper(contacts_x, contacts_y, ref_contact_map, ref_len, factor)
else:
atom_seqA = parse_pdb.get_atom_seq(open(pdb_filenameA, 'r'), chainA)
atom_seqB = parse_pdb.get_atom_seq(open(pdb_filenameB, 'r'), chainB)
atom_seq = atom_seqA + atom_seqB
align = pairwise2.align.globalms(atom_seq, seq, 2, -1, -0.5, -0.1)
alignA = pairwise2.align.globalms(atom_seqA, seqA, 2, -1, -0.5, -0.1)
alignB = pairwise2.align.globalms(atom_seqB, seqA, 2, -1, -0.5,
-0.1) # Align to seq A
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
atom_seq_aliA = alignA[-1][0]
seq_aliA = alignA[-1][1]
atom_seq_aliB = alignB[-1][0]
seq_aliB = alignB[-1][1]
gapped_cb_lst = []
gapped_cb_lstA = []
gapped_cb_lstB = []
ali_lst = []
ali_lstA = []
ali_lstB = []
j = 0
k = 0
for i in xrange(len(atom_seq_ali)):
#print i,j,k,seq_ali[i],atom_seq_ali[i]
if atom_seq_ali[i] == '-':
gapped_cb_lst.append(['-'])
ali_lst.append(-9999)
k += 1
elif seq_ali[i] == '-':
j += 1
continue
else:
ali_lst.append(j)
gapped_cb_lst.append(cb_lst[j])
k += 1
j += 1
j = 0
k = 0
for i in xrange(len(atom_seq_aliA)):
if atom_seq_aliA[i] == '-':
gapped_cb_lstA.append(['-'])
ali_lstA.append(-9999)
k += 1
elif seq_aliA[i] == '-':
j += 1
continue
else:
ali_lstA.append(j)
gapped_cb_lstA.append(cb_lstA[j])
k += 1
j += 1
j = 0
k = 0
for i in xrange(len(atom_seq_aliB)):
#print "B",i,j,k,seq_aliB[i],atom_seq_aliB[i]
if atom_seq_aliB[i] == '-':
gapped_cb_lstB.append(['-'])
ali_lstB.append(-9999)
k += 1
elif seq_aliB[i] == '-':
j += 1
continue
else:
ali_lstB.append(j)
gapped_cb_lstB.append(cb_lstB[j])
k += 1
j += 1
#print len(gapped_cb_lst),len(gapped_cb_lstA),len(gapped_cb_lstB)
dist_mat = get_cb_contacts(gapped_cb_lst)
dist_matA = get_cb_contacts(gapped_cb_lstA)
dist_matB = get_cb_contacts(gapped_cb_lstB)
cb_cutoff = 8
#ref_contact_map = dist_mat < cb_cutoff
# This routine adds all interface and B chain contacts
contacts_x, contacts_y, scores = get_interface_contacts(
contacts_x,
contacts_y,
scores,
dist_mat,
ref_lenA,
factor,
cb_cutoff + 4,
atom_seq_ali=atom_seq_ali)
ref_contact_map = dist_mat < cb_cutoff
ref_contact_mapA = dist_matA < cb_cutoff
ref_contact_mapB = dist_matB < cb_cutoff
# Here we need to append
if print_dist:
print_distances(contacts_x,
contacts_y,
scores,
dist_mat,
bfactor,
surf,
ref_lenA,
ref_lenB,
seq,
ali_lst=ali_lst,
atom_seq=atom_seq,
outfile=outfilename)
Zscore = get_Zscore(contacts_x,
contacts_y,
ref_contact_map,
scores,
atom_seq_ali=atom_seq_ali)
ZscoreA = get_Zscore(contactsA_x,
contactsA_y,
ref_contact_mapA,
scoresA,
atom_seq_ali=atom_seq_aliA)
ZscoreB = get_Zscore(contactsB_x,
contactsB_y,
ref_contact_mapB,
scoresB,
atom_seq_ali=atom_seq_aliB)
ZscoreI = get_Zscore_interface(contacts_x,
contacts_y,
ref_contact_map,
ref_lenA,
ref_lenB,
scores,
atom_seq_ali=atom_seq_ali)
PPV, TP, FP = get_ppv_helper(contacts_x,
contacts_y,
ref_contact_map,
ref_len,
factor,
atom_seq_ali=atom_seq_ali)
PPVa, TPa, FPa = get_ppv_helper(contactsA_x,
contactsA_y,
ref_contact_mapA,
interfacelen,
factor,
atom_seq_ali=atom_seq_aliA)
PPVb, TPb, FPb = get_ppv_helper(contactsB_x,
contactsB_y,
ref_contact_mapB,
interfacelen,
factor,
atom_seq_ali=atom_seq_aliB)
PPVi, TPi, FPi, PPViE, TPiE, FPiE = get_ppv_helper_interface(
contacts_x,
contacts_y,
ref_contact_map,
bfactor,
ref_lenA,
ref_lenB,
interfacelen,
cutoff,
atom_seq_ali=atom_seq_ali)
#for i in range(10):
# print "I: ",i,contactsI_x[i],contactsI_y[i],scoresI[i],dist_mat[contactsI_x[i]][contactsI_y[i]],ref_contact_map[contactsI_x[i]][contactsI_y[i]]
# print "A: ",i,contactsA_x[i],contactsA_y[i],scoresA[i],dist_mat[contactsA_x[i]][contactsA_y[i]],ref_contact_map[contactsA_x[i]][contactsA_y[i]]
# print "B: ",i,contactsB_x[i],contactsB_y[i],scoresB[i],dist_mat[contactsB_x[i]][contactsB_y[i]],ref_contact_map[contactsB_x[i]][contactsB_y[i]]
if name:
print '%s %s %s %s %s' % (name, PPVa, TPa, FPa, ZscoreA)
print '%s %s %s %s %s' % (name, PPVb, TPb, FPb, ZscoreB)
print '%s %s %s %s %s' % ("BOTH", PPV, TP, FP, Zscore)
print '%s %s %s %s %s' % ("Interface", PPVi, TPi, FPi, ZscoreI)
print '%s %s %s %s' % ("Interface-Exposed", PPViE, TPiE, FPiE)
else:
print '%s %s %s %s %s %s' % (fasta_filenameA, c_filename, PPVa, TPa,
FPa, ZscoreA)
print '%s %s %s %s %s %s' % (fasta_filenameB, c_filename, PPVb, TPb,
FPb, ZscoreB)
print '%s %s %s %s %s %s' % ("BOTH", c_filename, PPV, TP, FP, Zscore)
print '%s %s %s %s %s %s' % ("Interface", c_filename, PPVi, TPi, FPi,
ZscoreI)
print '%s %s %s %s %s' % ("Interface-Exposed", c_filename, PPViE, TPiE,
FPiE)
print 'PPV %s %s %s %s %s %s' % (c_filename, PPV, PPVa, PPVb, PPVi, PPViE)
print 'Zscore %s %s %s %s %s' % (c_filename, Zscore, ZscoreA, ZscoreB,
ZscoreI)
return (pdb_filenameA, PPV, TP, FP)
import sys
import argparse
import Bio.PDB
from Bio import pairwise2
from os.path import expanduser
home = expanduser("~")
sys.path.append(home + '/git/bioinfo-toolbox')
from parsing import parse_contacts
from parsing import parse_fasta
from parsing import parse_pdb
if __name__ == "__main__":
p = argparse.ArgumentParser(description='Get sequence identity from two fasta files.')
p.add_argument('fasta_fileA')
p.add_argument('fasta_fileB')
args = vars(p.parse_args(sys.argv[1:]))
fasta_filenameA = args['fasta_fileA']
fasta_filenameB = args['fasta_fileB']
seqA = parse_fasta.read_fasta(open(fasta_filenameA, 'r')).values()[0][0]
seqB = parse_fasta.read_fasta(open(fasta_filenameB, 'r')).values()[0][0]
align = pairwise2.align.localms(seqA,seqB , 1, -1, -0.5, -0.1)
minlen=len(seqA)
if len(seqB)<minlen:
minlen=len(seqB)
# print "Identity: ",fasta_filenameA,fasta_filenameB,float(align[0][2])/float(align[0][4]-align[0][3])
print "Identity: ",fasta_filenameA,fasta_filenameB,float(align[0][2])/float(minlen)
def get_dist(fasta_filename,
c_filename,
pdb_filename,
chain='',
sep='',
outfilename='',
noalign=False,
dist_type='CB'):
acc = fasta_filename.split('.')[-2][-5:-1]
### get sequence
seq = parse_fasta.read_fasta(open(fasta_filename, 'r')).values()[0][0]
ref_len = len(seq)
### get top "factor" * "ref_len" predicted contacts
contacts = parse_contacts.parse(open(c_filename, 'r'), sep, min_dist=5)
contacts_x = []
contacts_y = []
scores = []
count = 0
for i in range(len(contacts)):
score = contacts[i][0]
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
contacts_x.append(c_x)
contacts_y.append(c_y)
scores.append(score)
count += 1
res_lst = parse_pdb.get_coordinates(open(pdb_filename, 'r'), chain)
cb_lst = parse_pdb.get_cb_coordinates(open(pdb_filename, 'r'), chain)
ca_lst = parse_pdb.get_ca_coordinates(open(pdb_filename, 'r'), chain)
if noalign:
if dist_type == 'CB':
dist_mat = get_dist_mat(cb_lst)
elif dist_type == 'CA':
dist_mat = get_dist_mat(ca_lst)
else:
dist_mat = get_dist_mat_heavy(res_lst)
contacts_dist = get_dist_helper(contacts_x, contacts_y, dist_mat)
else:
atom_seq = parse_pdb.get_atom_seq(open(pdb_filename, 'r'), chain)
align = pairwise2.align.globalms(atom_seq, seq, 2, -1, -0.5, -0.1)
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
j = 0
gapped_res_lst = []
gapped_cb_lst = []
gapped_ca_lst = []
for i in xrange(len(atom_seq_ali)):
if atom_seq_ali[i] == '-':
gapped_res_lst.append('-')
gapped_cb_lst.append('-')
gapped_ca_lst.append('-')
elif seq_ali[i] == '-':
j += 1
continue
else:
gapped_res_lst.append(res_lst[j])
gapped_cb_lst.append(cb_lst[j])
gapped_ca_lst.append(ca_lst[j])
j += 1
assert (len(gapped_ca_lst) == len(gapped_cb_lst) ==
len(gapped_res_lst))
if dist_type == 'CB':
dist_mat = get_dist_mat(gapped_cb_lst)
elif dist_type == 'CA':
dist_mat = get_dist_mat(gapped_ca_lst)
else:
dist_mat = get_dist_mat_heavy(gapped_res_lst)
contacts_dist = get_dist_helper(contacts_x,
contacts_y,
dist_mat,
atom_seq_ali=atom_seq_ali)
assert (len(contacts_dist) == len(contacts_x) == len(contacts_y) ==
len(scores))
num_c = len(contacts_dist)
if outfilename:
with open(outfilename, 'w') as outfile:
for i in xrange(num_c):
outfile.write('%s %s %f %f\n' % (contacts_x[i], contacts_y[i],
scores[i], contacts_dist[i]))
return (contacts_x, contacts_y, scores, contacts_dist)
sys.path.append("/scratch/mirco_local/bioinfo-toolbox")
from parsing import parse_fasta
from parsing import parse_contacts
if len(sys.argv) != 5:
sys.stderr.write("Incorrect number of command line arguments.\n")
sys.stderr.write("Usage: " + sys.argv[0] + " <sequence file> <contact file> <CASP target ID> <output filename>\n\n")
sys.exit(0)
sfile = sys.argv[1]
cfile = sys.argv[2]
target = sys.argv[3]
seq = parse_fasta.read_fasta(open(sfile)).items()[0][1][0]
contacts = parse_contacts.parse(open(cfile), min_dist=0)
print len(contacts)
print contacts[0]
print seq
ofile = open(sys.argv[4], "w")
ofile.write(
"PFRMAT RR\nTARGET %s\nAUTHOR 6685-2065-9124\nMETHOD Pcons-net\nREMARK PconsC2\nMETHOD Improved contact predictions using the\nMETHOD recognition of protein like contact\nMETHOD patterns.\nMODEL 1\n"
% target
)
tmp_i = 1
def plot_map(fasta_filename, c_filename, factor, c2_filename='', psipred_horiz_fname='', psipred_vert_fname='', pdb_filename='', is_heavy=False, chain='', sep=',', outfilename=''):
acc = fasta_filename.split('.')[0][:4]
### get sequence
seq = parse_fasta.read_fasta(open(fasta_filename, 'r')).values()[0][0]
ref_len = len(seq)
### get top "factor" * "ref_len" predicted contacts
contacts = parse_contacts.parse(open(c_filename, 'r'), sep)
contacts_x = []
contacts_y = []
scores = []
contact_dict = {}
count = 0
for i in range(len(contacts)):
score = contacts[i][0]
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
pos_diff = abs(c_x - c_y)
too_close = pos_diff < 5
if not too_close:
contacts_x.append(c_x)
contacts_y.append(c_y)
scores.append(score)
count += 1
if count >= ref_len * factor:
break
### start plotting
fig = plt.figure()
ax = fig.add_subplot(111)
### plot secondary structure on the diagonal if given
if psipred_horiz_fname or psipred_vert_fname:
if psipred_horiz_fname:
ss = parse_psipred.horizontal(open(psipred_horiz_fname, 'r'))
else:
ss = parse_psipred.vertical(open(psipred_vert_fname, 'r'))
assert len(ss) == ref_len
for i in range(len(ss)):
if ss[i] == 'H':
plt.plot(i, i, 'o', c='#8B0043', mec="#8B0043", markersize=2)
if ss[i] == 'E':
plt.plot(i, i, 'D', c='#0080AD', mec="#0080AD", markersize=2)
if ss[i] == 'C':
continue
### plot reference contacts in the background if given
if pdb_filename:
res_lst = parse_pdb.get_coordinates(open(pdb_filename, 'r'), chain)
cb_lst = parse_pdb.get_cb_coordinates(open(pdb_filename, 'r'), chain)
atom_seq = parse_pdb.get_atom_seq(open(pdb_filename, 'r'), chain)
align = pairwise2.align.globalms(atom_seq, seq, 2, -1, -0.5, -0.1)
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
j = 0
gapped_res_lst = []
gapped_cb_lst = []
for i in xrange(len(atom_seq_ali)):
if atom_seq_ali[i] == '-':
gapped_res_lst.append('-')
gapped_cb_lst.append('-')
elif seq_ali[i] == '-':
j += 1
continue
else:
gapped_res_lst.append(res_lst[j])
gapped_cb_lst.append(cb_lst[j])
j += 1
if is_heavy:
dist_mat = get_heavy_contacts(gapped_res_lst)
heavy_cutoff = 5
ref_contact_map = dist_mat < heavy_cutoff
ref_contacts = np.where(dist_mat < heavy_cutoff)
else:
dist_mat = get_cb_contacts(gapped_cb_lst)
cb_cutoff = 8
ref_contact_map = dist_mat < cb_cutoff
ref_contacts = np.where(dist_mat < cb_cutoff)
ref_contacts_x = ref_contacts[0]
ref_contacts_y = ref_contacts[1]
PPVs, TPs, FPs = get_ppvs(contacts_x, contacts_y, ref_contact_map, atom_seq_ali, ref_len, factor)
tp_colors = get_tp_colors(contacts_x, contacts_y, ref_contact_map, atom_seq_ali)
print '%s %s %s %s' % (pdb_filename, PPVs[-1], TPs[-1], FPs[-1])
ax.scatter(ref_contacts_x, ref_contacts_y, marker='o', c='#CCCCCC', lw=0, edgecolor='#CCCCCC')
### plot predicted contacts from second contact map if given
if c2_filename:
contacts2 = parse_contacts.parse(open(c2_filename, 'r'), sep)
contacts2_x = []
contacts2_y = []
scores2 = []
contact_dict2 = {}
count = 0
for i in range(len(contacts2)):
score = contacts2[i][0]
c_x = contacts2[i][1] - 1
c_y = contacts2[i][2] - 1
pos_diff = abs(c_x - c_y)
too_close = pos_diff < 5
if not too_close:
contacts2_x.append(c_x)
contacts2_y.append(c_y)
scores2.append(score)
count += 1
if count >= ref_len * factor:
break
### use TP/FP color coding if reference contacts given
if pdb_filename:
PPVs2, TPs2, FPs2 = get_ppvs(contacts2_x, contacts2_y, ref_contact_map, atom_seq_ali, ref_len, factor)
tp2_colors = get_tp_colors(contacts2_x, contacts2_y, ref_contact_map, atom_seq_ali)
print '%s %s %s %s' % (pdb_filename, PPVs2[-1], TPs2[-1], FPs2[-1])
fig.suptitle('%s\nPPV (upper left) = %.2f | PPV (lower right) = %.2f' % (acc, PPVs[-1], PPVs2[-1]))
sc = ax.scatter(contacts2_y[::-1], contacts2_x[::-1], marker='o', c=tp2_colors[::-1], s=6, alpha=0.75, linewidths=0.0)
sc = ax.scatter(contacts_x[::-1], contacts_y[::-1], marker='o', c=tp_colors[::-1], s=6, alpha=0.75, linewidths=0.0)
else:
sc = ax.scatter(contacts2_y[::-1], contacts2_x[::-1], marker='o', c='#D70909', edgecolor='#D70909', s=4, linewidths=0.5)
sc = ax.scatter(contacts_x[::-1], contacts_y[::-1], marker='o', c='#004F9D', edgecolor='#004F9D', s=4, linewidths=0.5)
### plot predicted contacts from first contact map on both triangles
### if no second contact map given
else:
if pdb_filename:
fig.suptitle('%s\nPPV = %.2f' % (acc, PPVs[-1]))
sc = ax.scatter(contacts_x[::-1], contacts_y[::-1], marker='o', c=tp_colors[::-1], s=6, alpha=0.75, linewidths=0.0)
sc = ax.scatter(contacts_y[::-1], contacts_x[::-1], marker='o', c=tp_colors[::-1], s=6, alpha=0.75, linewidths=0.0)
else:
sc = ax.scatter(contacts_x[::-1], contacts_y[::-1], marker='o', c=scores[::-1], s=4, alpha=0.75, cmap=cm.jet, linewidths=0.1)
sc = ax.scatter(contacts_y[::-1], contacts_x[::-1], marker='o', c=scores[::-1], s=4, alpha=0.75, cmap=cm.jet, linewidths=0.1)
plt.colorbar(sc)
plt.gca().set_xlim([0,ref_len])
plt.gca().set_ylim([0,ref_len])
if outfilename:
if outfilename.endswith('.pdf'):
pp = PdfPages(outfilename)
pp.savefig(fig)
pp.close()
elif outfilename.endswith(('.png', '.jpg', '.jpeg')):
plt.savefig(outfilename)
else:
pp = PdfPages('%s.pdf' % outfilename)
pp.savefig(fig)
pp.close()
else:
pp = PdfPages('%s_ContactMap.pdf' % c_filename)
pp.savefig(fig)
pp.close()
