def load_distmap(fname):
return DistanceMap.from_file(fname)
def get_strand_pairings(list_beta_pairs,
ec_file,
init_struct=False,
chain_id=' '):
#Inputs:
#list_beta_pairs: e.g. [(1,10),(23,43)]
#ecs: pandas Dataframe with ecs
# Load in ecs file
ecs = pd.read_csv(ec_file)
#max_linker = 10
max_linker = 6
#ec_cutoff = ecs.iloc[2*max(ecs['j'])]['cn']
ec_cutoff = ecs.iloc[min(len(ecs),
(max(ecs['j']) - min(ecs['i']) + 1))]['cn']
num_betas = len(list_beta_pairs)
# Create list of pairs that can be updated
strand_pairs = {}
# Sort beta strands in case they're out of order
list_beta_pairs = sorted(list_beta_pairs, key=lambda x: x[0])
# Load in our predicted initial structure and get a dataframe with distance coords for each pairing
if init_struct:
coords = ClassicPDB.from_file(init_struct).get_chain(chain_id)
dist_map = DistanceMap.from_coords(coords)
ecs_with_dist = coupling_scores_compared(ecs, dist_map)
# Check if any strands are too close together and must be coupled
#length_linkers = []
for i in range(num_betas - 1):
length_linker = list_beta_pairs[i + 1][0] - list_beta_pairs[i][1] - 1
if length_linker < max_linker:
#print('Linker: {} to {}'.format(i+1,i))
if i in strand_pairs:
strand_pairs[i] = strand_pairs[i] + [
(i + 1, ('antiparallel', 'linker'))
]
else:
strand_pairs[i] = [(i + 1, ('antiparallel', 'linker'))]
#strand_pairs[i] = [(i+1,('antiparallel','linker'))]
if (i + 1) in strand_pairs:
strand_pairs[i + 1] = strand_pairs[i + 1] + [
(i, ('antiparallel', 'linker'))
]
else:
strand_pairs[i + 1] = [(i, ('antiparallel', 'linker'))]
#strand_pairs[i+1] = [(i,('antiparallel','linker'))]
#print(strand_pairs)
# Now that we've eliminated clashes based on linker length, let's go through top ECs
# and choose strand pairings on basis of top EC
for i in range(num_betas):
# Creating temporary lists of top ec scores, and the strand index the EC forms a pair with
top_ec_score_list = []
indices = []
ec_pairs = []
# Go through each potential partner strand
for j in range(num_betas):
# Don't compare same-strand ecs
if j == i:
continue
indices.append(j)
b1_start = list_beta_pairs[i][0]
b1_end = list_beta_pairs[i][1]
b2_start = list_beta_pairs[j][0]
b2_end = list_beta_pairs[j][1]
# Get ECs between our strand of interest and its current partner strand, and append top scoring EC to list
temp_list = ecs.query(
'(i >= @b1_start and i <= @b1_end and j >= @b2_start and j<= @b2_end) or (j >= @b1_start and j <= @b1_end and i >= @b2_start and i<=@b2_end)'
)
if len(temp_list) > 0:
top_ec = temp_list.iloc[0]['cn']
top_ec_i = temp_list.iloc[0]['i']
top_ec_j = temp_list.iloc[0]['j']
else:
top_ec = 0
top_ec_j = 0
top_ec_i = 0
#top_ec = ecs.query('(i >= @b1_start and i <= @b1_end and j >= @b2_start and j<= @b2_end) or (j >= @b1_start and j <= @b1_end and i >= @b2_start and i<=@b2_end)').iloc[0]
#cn = top_ec['cn']
cn = top_ec
top_ec_score_list.append(cn)
ec_pairs.append((top_ec_i, top_ec_j))
#print(top_ec_score_list)
# Get strand identifier and EC score for best and second-best strand-strand pairings
best_strand = indices[np.argmax(top_ec_score_list)]
max_ec = max(top_ec_score_list)
sbi = [
top_ec_score_list.index(x)
for x in sorted(top_ec_score_list, reverse=True)
][1]
second_best_strand = indices[sbi]
second_best_ec = [x
for x in sorted(top_ec_score_list, reverse=True)][1]
# Guess whether parallel or antiparallel based on surrounding pair scores
def _check_parallel(ec_pair, ecs, list_beta_pairs, istrand, jstrand):
b1_start = list_beta_pairs[istrand][0]
b1_end = list_beta_pairs[istrand][1]
b2_start = list_beta_pairs[jstrand][0]
b2_end = list_beta_pairs[jstrand][1]
i_val = ec_pair[0]
j_val = ec_pair[1]
#print(str(list_beta_pairs[istrand])+' '+str(list_beta_pairs[jstrand]))
strand_ecs = ecs.query(
'(i>=@b1_start and i<=@b1_end and i!=@i_val and j>=@b2_start and j<=@b2_end and j!=@j_val) or (j>=@b1_start and j<=@b1_end and j!=@j_val and i>=@b2_start and i<=@b2_end and i!=@i_val )'
).sort_values(by='cn', ascending=False)
if len(strand_ecs) < 1:
#print('not enough ecs')
#print(strand_ecs)
return 'not enough info to determine strand orientation'
new_i = strand_ecs.iloc[0]['i']
new_j = strand_ecs.iloc[0]['j']
if (i_val - new_i) * (j_val - new_j) < 0:
return 'antiparallel'
elif (i_val - new_i) * (j_val - new_j) > 0:
return 'parallel'
else:
print(strand_ecs)
return 'Error: dx 0'
# TODO: add check if this goes beyond length of protein sequence
#antiparallel_score = float(ecs.query('i==(@i_val-1) and j==(@j_val+1)').iloc[0]['cn'])+float(ecs.query('i==(@i_val+1) and j==(@j_val-1)').iloc[0]['cn'])
#parallel_score = float(ecs.query('i==(@i_val-1) and j==(@j_val-1)')['cn'])+float(ecs.query('i==(@i_val+1) and j==(@j_val+1)')['cn'])
#if parallel_score > antiparallel_score:
# return 'parallel'
#else:
# return 'antiparallel'
#best_strand_orientation = _check_parallel(ec_pairs[np.argmax(top_ec_score_list)],ecs)
#second_best_strand_orientation =_check_parallel(ec_pairs[sbi],ecs)
best_strand_orientation = _check_parallel(
ec_pairs[np.argmax(top_ec_score_list)], ecs, list_beta_pairs, i,
best_strand)
second_best_strand_orientation = _check_parallel(
ec_pairs[sbi], ecs, list_beta_pairs, i, second_best_strand)
# If EC score is below cutoff, don't include
if max_ec < ec_cutoff:
max_ec = 'below_cutoff'
if second_best_ec < ec_cutoff:
second_best_ec = 'below_cutoff'
# If we haven't already added the strand based on linker constraints, put in both best and second-best pairings
if i not in strand_pairs:
strand_pairs[i] = [
(best_strand, (best_strand_orientation, max_ec)),
(second_best_strand, (second_best_strand_orientation,
second_best_ec))
]
# If we already have a strand-strand pairing based on linker constraints, put in the pairing suggested by
#either best or second-best EC based on which one is already present
elif len(strand_pairs[i]) == 1:
if strand_pairs[i][0][0] == best_strand:
strand_pairs[i] = strand_pairs[i] + [
(second_best_strand,
(second_best_strand_orientation, second_best_ec))
]
else:
strand_pairs[i] = strand_pairs[i] + [
(best_strand, (best_strand_orientation, max_ec))
]
# If we've already identified two partners for this strand on the basis of linker, don't add anything else
else:
continue
# prune list to make sure we don't have any one-directional pairings, e.g. 1->3 but not 3->1
for i in range(len(strand_pairs)):
s1 = strand_pairs[i][0][0]
s2 = strand_pairs[i][1][0]
if i not in [strand_pairs[s1][0][0], strand_pairs[s1][1][0]]:
#print('{}: in {} or {}?'.format(i,strand_pairs[s1][0][0],strand_pairs[s1][0][1]))
strand_pairs[i][0] = (strand_pairs[i][0][0],
(strand_pairs[i][0][1][0],
'one_directional'))
if i not in [strand_pairs[s2][0][0], strand_pairs[s2][1][0]]:
strand_pairs[i][1] = (strand_pairs[i][1][0],
(strand_pairs[i][1][1][0],
'one_directional'))
return strand_pairs, list_beta_pairs