for seq in seqs:

for i in range(len(seq)):

amino = seq[i]

if amino not in PSSM_profile:

PSSM_profile[amino] = [pseudocount for x in range(len(seq))]

PSSM_profile[amino][i] += 1

else:

PSSM_profile[amino][i] += 1

# calc score

scored_profile = {}

for k, v in PSSM_profile.items():

scored_profile[k] = [round(element/(len(seqs[0]) + pseudocount*len(PSSM_profile.keys())), 3) for element in v]

_sum = sum(scored_profile[k])

scored_profile[k] = [round(math.log(round(element/(_sum/len(seqs[0])), 3), 2), 3) for element in scored_profile[k]]

listsub = list(fullset)

subsets = []

for i in range(2**len(listsub)):

subset = []

for k in range(len(listsub)):

if i & 1<<k:

subset.append(listsub[k])

subsets.append(subset)

score = 0

for i in range(len(sub_seq)):

score += pssm_profile[sub_seq[i]][i]

best_score = -1*math.inf

best_sub_target = None

power_set = calc_powerset(set([i for i in range(window_size)]))

for gap_num in range(window_size):

char_num = window_size - gap_num

for i in range(len(target_seq) - char_num + 1):

tar_part = target_seq[i:i+char_num]

# insert gaps

for sub_p_s in power_set:

if len(sub_p_s) == gap_num:

tar_part_temp = copy.deepcopy(tar_part)

for sp in sub_p_s:

tar_part_temp = tar_part_temp[:sp] + '-' + tar_part_temp[sp:]

score = calc_score(tar_part_temp, pssm_profile)

if score > best_score:

best_score = score

best_sub_target = tar_part_temp