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 get_scores_from_contacts(c_filename, min_dist, factor_value, min_score, ref_len):
""" Return a tupla unpacking of three lists,
[contacts_x], [contacts_y], [scores]. """
# get separator from c_filename
sep = get_separator(c_filename)
# get a list ranked predicted contacts for those
# carbon-beta (CB) that are 5 residues separated.
# In the function "parse_contacts.parse()", min_dist is 5 for default.
# This returns parse_contacts.parse: [(score, resA_CB, resB_CB)]
contacts = parse_contacts.parse(c_filename, sep, min_dist)
# Build a list for each residue numparseber
# e.g.: from resA_CB to resA_numN ---> contacts_x = [resA_num1, ..., resA_numN]
# e.g.: from resB_CB to resB_numN ---> contacts_y = [resB_num1, ..., resB_numN]
contacts_x = []
contacts_y = []
# Build a score list
scores = []
num_c = len(contacts)
count = 0
for i in range(num_c):
score = contacts[i][0]
# It use "- 1" because the calling gives the real biological position
# of the residue and python start counting at zero.
c_x = contacts[i][1] - 1
c_y = contacts[i][2] - 1
# Calculate the distance in the sequence position between resA and resB
pos_diff = abs(c_x - c_y)
# Boolean declaration with the distances.
# Check if those are less than 5 residues far from each other.
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
# Check if the contact predicted is below than min_score
# and the count are grater or equal than ref_len*factor_value (default, len*1.0)
if min_score == -1.0 and count >= ref_len * factor_value:
break
if score < min_score:
break
return contacts_x, contacts_y, scores
# 10 steps during the run, and write stats every 10 steps
md.optimize(atmsel,
temperature=300,
max_iterations=50,
actions=[
actions.write_structure(10, query_id + '.D9998%04d.pdb'),
actions.trace(10, trcfil)
])
# Finish off with some more CG, and write stats every 5 steps
cg.optimize(atmsel, max_iterations=20, actions=[actions.trace(5, trcfil)])
mpdf = atmsel.energy()
mdl.write(file=query_id + '.D00000001.pdb')
contacts = parse_contacts.parse(open(contact_filename, 'r'))
count = 0
seq_len = len(aln[query_id])
for (score, i, j) in contacts:
rsr.add(
forms.gaussian(group=physical.xy_distance,
feature=features.distance(mdl.atoms['CA:%d' % i],
mdl.atoms['CA:%d' % j]),
mean=10.0,
stdev=2))
#rsr.add(MyFade(group=physical.xy_distance,
# feature=features.distance(mdl.atoms['CA:%d' % i],
# mdl.atoms['CA:%d' % j]),
# cutoff_lower=-100, cutoff_upper=100, fade_zone=92, well_depth=-150))
is_gly_a = aln[query_id].residues[i - 1].code == 'G'
is_gly_b = aln[query_id].residues[j - 1].code == 'G'
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()
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(
"PFRMAT RR\nTARGET %s\nAUTHOR 4146-6019-9011\nMETHOD PconsC2\nREMARK PconsC2\nMETHOD Improved contact predictions using the\nMETHOD recognition of protein like contact\nMETHOD patterns.\nMODEL 1\n"
% target)
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
sys.path.append("/home/mircomic/toolbox")
from parsing import parse_contacts
# command line input
infile_name = sys.argv[1]
# guessing separator of constraint file
test_line = open(infile_name,'r').readline()
if len(test_line.split(',')) != 1:
sep = ','
elif len(test_line.split(' ')) != 1:
sep = ' '
else:
sep = '\t'
# parse constraint file
c_list = parse_contacts.parse(open(infile_name, 'r'))
# sort contacts and write simple string
for c in c_list:
print ('%s %s %s' % (c[1], c[2], c[0]))
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)
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
for aa in seq:
ofile.write(aa)
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()
def plot_map(fasta_filename, c_filename, factor, c2_filename='', psipred_filename='', pdb_filename='', is_heavy=False, chain='', sep='', sep2='', outfilename=''):
acc = c_filename.split('.')[0]
### get sequence
#seq = parse_fasta.read_fasta(open(fasta_filename, 'r')).values()[0][0]
#ref_len = len(seq)
### get id
f = open(fasta_filename,"rU")
seqs = SeqIO.parse(f,"fasta")
# we assume there is only one record
for record in seqs:
seq = str(record.seq)
protein_id = record.id
ref_len = len(seq)
# guessing separator of constraint file
if sep == '':
line = open(c_filename,'r').readline()
if len(line.split(',')) != 1:
sep = ','
elif len(line.split(' ')) != 1:
sep = ' '
else:
sep = '\t'
### 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()
plt.title('Contact map for ' + protein_id)
ax = fig.add_subplot(111)
### plot secondary structure on the diagonal if given
if psipred_filename:
ss = parse_psipred.horizontal(open(psipred_filename, 'r'))
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)
if (len(res_lst)==0) or (len(cb_lst)==0):
print "Could not parse the PDB file, res_list or cb_list is empty"
return
try:
atom_seq_ali = align[-1][0]
seq_ali = align[-1][1]
except Exception,ex:
print "Could not parse the PDB file:", ex
return
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 = 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\t%s' % (acc, PPVs[-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:
# guessing separator of constraint file
if sep2 == '':
line = open(c_filename,'r').readline()
if len(line.split(',')) != 1:
sep2 = ','
elif len(line.split(' ')) != 1:
sep2 = ' '
else:
sep2 = '\t'
contacts2 = parse_contacts.parse(open(c2_filename, 'r'), sep2)
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
