def s2NOEfit(transformed_coors, native_sse_order, exp_data):
import warnings
"""
Depr
:param transformed_coors:
:param native_sse_order:
:param exp_data:
:return:
"""
warnings.warn("deprecated", DeprecationWarning)
noe_cutoff = False
sse_coors = copy.deepcopy(transformed_coors)
noe_matrix = exp_data['noe_data']
noes_found = []
noes_total = []
for i in range(0, len(sse_coors) - 1):
res_c, ca1 = getNHandresi(sse_coors[i])
res_n, ca2 = getNHandresi(sse_coors[i + 1])
ss1_list = range(native_sse_order[i][4], native_sse_order[i][5] + 1)
ss2_list = range(native_sse_order[i + 1][4],
native_sse_order[i + 1][5] + 1)
try:
for res1 in ss1_list:
# print ss1_list, res1, res_c, res_c[ss1_list.index(res1)]
# print ss1_list.index(res1)
ca_res1 = [
ca1[0][ss1_list.index(res1)], ca1[1][ss1_list.index(res1)],
ca1[2][ss1_list.index(res1)]
]
for res2 in ss2_list:
if noe_matrix[res1, res2]:
noe_cutoff = noe_matrix[res1, res2]
elif noe_matrix[res2, res1]:
noe_cutoff = noe_matrix[res2, res1]
else:
noe_cutoff = False
if noe_cutoff:
ca_res2 = [
ca2[0][ss2_list.index(res2)],
ca2[1][ss2_list.index(res2)],
ca2[2][ss2_list.index(res2)]
]
dist = get_dist(ca_res1, ca_res2)
if dist < noe_cutoff:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
except:
return []
if len(noes_found) == 0:
return []
fmeasure = calcFmeasure(noes_found, noes_total)
return fmeasure
def S1ILVANOEprob(s1_def, s2_def, smotif, exp_data):
"""
:param s1_def:
:param s2_def:
:param smotif:
:param exp_data:
:return:
"""
noe_data = exp_data['noe_data']
print "Working on new one"
print noe_data
noes = noe_data[0]
total_noes = noe_data[1]
noes_found = 0.0
smotif_noes = 0.0
ss1_list = range(s1_def[4], s1_def[5] + 1)
ss2_list = range(s2_def[4], s2_def[5] + 1)
#smotif_ss1 = range(int(smotif[0][1]), int(smotif[0][2]) + 1)
#smotif_ss2 = range(int(smotif[0][3]), int(smotif[0][4]) + 1)
coor_matrix = getHCoorMatrix(ss1_list, smotif[1])
coor2_matrix = getHCoorMatrix(ss2_list, smotif[2])
coor_matrix.update(coor2_matrix)
resi = coor_matrix.keys()
for noe in noes:
res1, atm1, res2, atom2, noe_cutoff, tol = noe[0], noe[1], noe[2], noe[
3], noe[4], noe[5]
if res1 in resi and res2 in resi:
smotif_noes += 1.0
coo1 = coor_matrix[res1]
coo2 = coor_matrix[res2]
dist = get_dist(coo1, coo2)
if dist <= noe_cutoff + tol:
noes_found += 1.0
if smotif_noes > 0:
local_prob = (noes_found / smotif_noes)
else:
local_prob = 0.0
return (noes_found / total_noes), local_prob, noes_found
def SxNOEprob(transformed_coors, native_sse_order, current_ss, exp_data):
"""
:param transformed_coors:
:param native_sse_order:
:param current_ss:
:param exp_data:
:return:
"""
import copy
sse_coors = copy.deepcopy(transformed_coors)
noe_data = exp_data['noe_data']
noes = noe_data[0]
total_noes = noe_data[1]
noes_found = 0.0
smotif_noes = 0.0
coor_matrix = {}
for i in range(0, len(sse_coors)):
tcoor = getSxCoorMatrix(sse_coors[i], native_sse_order[i])
coor_matrix.update(tcoor)
resi = coor_matrix.keys()
for noe in noes:
res1, res2, noe_cutoff, tol = noe[0], noe[2], noe[4], noe[5]
if (res1 in resi) and (
res2 in resi): # this make sure that the resi are in the SSEs
smotif_noes += 1.0
coo1 = coor_matrix[res1]
coo2 = coor_matrix[res2]
dist = get_dist(coo1, coo2)
if dist <= noe_cutoff + tol:
noes_found += 1.0
if smotif_noes > 0:
local_prob = (noes_found / smotif_noes)
else:
local_prob = 0.0
#return (noes_found / total_noes), local_prob, noes_found
return local_prob, local_prob, noes_found
def s2EVcouplings(transformed_coors, native_sse_order, contact_matrix, plm_score_matrix, contacts_cutoff):
# print native_sse_order
import copy
sse_coors = copy.deepcopy(transformed_coors)
contacts_found = []
total_contacts = []
for i in range(0, len(sse_coors) - 1):
res_c, ca1 = getCAandresi(sse_coors[i])
res_n, ca2 = getCAandresi(sse_coors[i + 1])
ss1_list = range(native_sse_order[i][4], native_sse_order[i][5] + 1)
ss2_list = range(native_sse_order[i + 1][4], native_sse_order[i + 1][5] + 1)
for res1 in ss1_list:
# print ss1_list, res1, res_c, res_c[ss1_list.index(res1)]
# print ss1_list.index(res1)
ca_res1 = [ca1[0][ss1_list.index(res1)], ca1[1][ss1_list.index(res1)], ca1[2][ss1_list.index(res1)]]
for res2 in ss2_list:
ca_res2 = [ca2[0][ss2_list.index(res2)], ca2[1][ss2_list.index(res2)], ca2[2][ss2_list.index(res2)]]
dist = get_dist(ca_res1, ca_res2)
if contact_matrix[res1, res2] or contact_matrix[res2, res1]:
total_contacts.append((res1, res2))
if dist < contacts_cutoff:
contacts_found.append((res1, res2))
# print len(total_contacts), total_contacts
# print len(contacts_found), contacts_found
fmeasure = calcFmeasure(contacts_found, total_contacts)
if fmeasure:
plm_score = calcPLMscore(contacts_found, plm_score_matrix)
return fmeasure, plm_score
else:
return 0.0, 0.0
def s2NOEfit(transformed_coors, native_sse_order, exp_data):
noe_cutoff = 5.0
import copy
sse_coors = copy.deepcopy(transformed_coors)
noe_matrix = exp_data['noe_data']
noes_found = []
noes_total = []
for i in range(0, len(sse_coors) - 1):
res_c, ca1 = getNHandresi(sse_coors[i])
res_n, ca2 = getNHandresi(sse_coors[i + 1])
ss1_list = range(native_sse_order[i][4], native_sse_order[i][5] + 1)
ss2_list = range(native_sse_order[i + 1][4], native_sse_order[i + 1][5] + 1)
try:
for res1 in ss1_list:
# print ss1_list, res1, res_c, res_c[ss1_list.index(res1)]
# print ss1_list.index(res1)
ca_res1 = [ca1[0][ss1_list.index(res1)], ca1[1][ss1_list.index(res1)], ca1[2][ss1_list.index(res1)]]
for res2 in ss2_list:
ca_res2 = [ca2[0][ss2_list.index(res2)], ca2[1][ss2_list.index(res2)], ca2[2][ss2_list.index(res2)]]
dist = get_dist(ca_res1, ca_res2)
if noe_matrix[res1, res2] or noe_matrix[res2, res1]:
noes_total.append((res1, res2))
if dist < noe_cutoff:
noes_found.append((res1, res2))
except:
return []
if len(noes_found) == 0:
return []
fmeasure = calcFmeasure(noes_found, noes_total)
return fmeasure
def s1NOEfit(s1_def, s2_def, smotif, exp_data):
"""
:param s1_def:
:param s2_def:
:param smotif:
:param exp_data:
:return:
"""
noe_matrix = exp_data['noe_data']
ss1_list = range(s1_def[4], s1_def[5] + 1)
ss2_list = range(s2_def[4], s2_def[5] + 1)
smotif_ss1 = range(int(smotif[0][1]), int(smotif[0][2]) + 1)
smotif_ss2 = range(int(smotif[0][3]), int(smotif[0][4]) + 1)
noes_found = []
noes_total = []
# Scoring intra-SSE NOEs
coo1 = [999, 999, 999]
coo2 = [0.0, 0.0, 0.0]
for i in range(0, len(smotif_ss1) - 1):
sres1 = smotif_ss1[i]
nres1 = ss1_list[i]
for j in range(i + 1, len(smotif_ss1)):
sres2 = smotif_ss1[j]
nres2 = ss1_list[j]
if noe_matrix[nres1, nres2]:
noe_cutoff = noe_matrix[nres1, nres2]
for entry1 in smotif[1]:
if entry1[2] == 'H' and entry1[0] == sres1:
coo1 = [entry1[3], entry1[4], entry1[5]]
if entry1[2] == 'H' and entry1[0] == sres2:
coo2 = [entry1[3], entry1[4], entry1[5]]
dist = get_dist(coo1, coo2)
# print sres1, sres2, noe_cutoff, coo1, coo2, dist
if noe_cutoff > 10000:
real_noe = noe_cutoff - 10000
# backmapping side chain noes to amides
if real_noe - 4.0 <= dist <= real_noe + 4.0:
noes_found.append((sres1, sres2))
noes_total.append((sres1, sres2))
else:
noes_total.append((sres1, sres2))
elif dist <= noe_cutoff:
noes_found.append((sres1, sres2))
noes_total.append((sres1, sres2))
else:
noes_total.append((sres1, sres2))
coo1 = [999, 999, 999]
coo2 = [0.0, 0.0, 0.0]
for i in range(0, len(smotif_ss2) - 1):
sres1 = smotif_ss2[i]
nres1 = ss2_list[i]
for j in range(i + 1, len(smotif_ss2)):
sres2 = smotif_ss2[j]
nres2 = ss2_list[j]
if noe_matrix[nres1, nres2]:
noe_cutoff = noe_matrix[nres1, nres2]
for entry1 in smotif[2]:
if entry1[2] == 'H' and entry1[0] == sres1:
coo1 = [entry1[3], entry1[4], entry1[5]]
if entry1[2] == 'H' and entry1[0] == sres2:
coo2 = [entry1[3], entry1[4], entry1[5]]
dist = get_dist(coo1, coo2)
# print sres1, sres2, noe_cutoff, coo1, coo2, dist
if noe_cutoff > 10000:
real_noe = noe_cutoff - 10000
# backmapping side chain noes to amides
if real_noe - 4.0 <= dist <= real_noe + 4.0:
noes_found.append((sres1, sres2))
noes_total.append((sres1, sres2))
else:
noes_total.append((sres1, sres2))
elif dist <= noe_cutoff:
noes_found.append((sres1, sres2))
noes_total.append((sres1, sres2))
else:
noes_total.append((sres1, sres2))
# end of Scoring intra-SSE NOEs
for res in smotif_ss1:
for entry1 in smotif[1]:
if entry1[2] == 'H' and entry1[0] == res:
coo1 = [entry1[3], entry1[4], entry1[5]]
for entry2 in smotif[2]:
if entry2[2] == 'H':
coo2 = [entry2[3], entry2[4], entry2[5]]
res1 = ss1_list[smotif_ss1.index(entry1[0])]
res2 = ss2_list[smotif_ss2.index(entry2[0])]
if noe_matrix[res1, res2]:
noe_cutoff = noe_matrix[res1, res2]
else:
noe_cutoff = False
if noe_cutoff:
dist = get_dist(coo1, coo2)
if noe_cutoff > 10000:
real_noe = noe_cutoff - 10000
# backmapping side chain noes to amides
if real_noe - 4.0 <= dist <= real_noe + 4.0:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
elif dist <= noe_cutoff:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
if len(noes_found) == 0:
return 0.00, 0.00
# fmeasure = calcFmeasure(noes_found, noes_total)
fmeasure = (float(len(noes_found)) / float(len(noes_total)))
return fmeasure, len(noes_found)
def sXGlobalNOEfit(transformed_coors, native_sse_order, current_ss, exp_data):
import copy
sse_coors = copy.deepcopy(transformed_coors)
noe_matrix = exp_data['noe_data']
noes_found = []
noes_total = []
# current_ss ['strand', 13, 4, 10, 36, 48]
cresi = range(current_ss[4], current_ss[5] + 1)
# Score Intra SSE NOEs first
score_intra_sse = False
for i in range(0, len(sse_coors)):
ss1_list = range(native_sse_order[i][4], native_sse_order[i][5] + 1)
res_c, ca1 = getNHandresi(sse_coors[i])
try:
for j in range(0, len(cresi) - 1):
res1 = cresi[j]
coo1 = [
ca1[0][ss1_list.index(res1)], ca1[1][ss1_list.index(res1)],
ca1[2][ss1_list.index(res1)]
]
for k in range(j + 1, len(cresi)):
res2 = cresi[k]
coo2 = [
ca1[0][ss1_list.index(res2)],
ca1[1][ss1_list.index(res2)],
ca1[2][ss1_list.index(res2)]
]
noe_cutoff = noe_matrix[res1, res2]
if noe_cutoff:
dist = get_dist(coo1, coo2)
if noe_cutoff > 10000:
real_noe = noe_cutoff - 10000
# backmapping side chain noes to amides
if real_noe - 4.0 <= dist <= real_noe + 4.0:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
elif dist <= noe_cutoff:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
except:
continue
import itertools
sse_pairs = list(itertools.combinations(range(len(sse_coors)), 2))
# [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (2, 3),\
# (2, 4), (2, 5), (2, 6), (2, 7), (3, 4), (3, 5), (3, 6), (3, 7), (4, 5), (4, 6), (4, 7), (5, 6), (5, 7), (6, 7)]
for ij in sse_pairs:
i = ij[0]
j = ij[1]
res_c, ca1 = getNHandresi(sse_coors[i])
res_n, ca2 = getNHandresi(sse_coors[j])
ss1_list = range(native_sse_order[i][4], native_sse_order[i][5] + 1)
ss2_list = range(native_sse_order[j][4], native_sse_order[j][5] + 1)
for res1 in ss1_list:
try:
ca_res1 = [
ca1[0][ss1_list.index(res1)], ca1[1][ss1_list.index(res1)],
ca1[2][ss1_list.index(res1)]
]
except:
continue
for res2 in ss2_list:
if noe_matrix[res1, res2]:
noe_cutoff = noe_matrix[res1, res2]
elif noe_matrix[res2, res1]:
noe_cutoff = noe_matrix[res2, res1]
else:
noe_cutoff = False
if noe_cutoff:
# mo res1, res2, noe_matrix[res1, res2], noe_matrix[res2, res1], noe_cutoff
try:
ca_res2 = [
ca2[0][ss2_list.index(res2)],
ca2[1][ss2_list.index(res2)],
ca2[2][ss2_list.index(res2)]
]
except:
continue
dist = get_dist(ca_res1, ca_res2)
if noe_cutoff > 10000:
real_noe = noe_cutoff - 10000 # back mapping side chain noes to amides
if real_noe - 4.0 <= dist <= real_noe + 4.0:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
elif dist <= noe_cutoff:
noes_found.append((res1, res2))
noes_total.append((res1, res2))
else:
noes_total.append((res1, res2))
# if len(noes_found) == 0 or not sse_satisfied:
if len(noes_found) == 0:
return 0.00, 0
# fmeasure = calcFmeasure(noes_found, noes_total)
fmeasure = (float(len(noes_found)) / float(len(noes_total)))
return fmeasure, len(noes_found)
