def compare_designs(args):
all_seqs = read_seqs(args['fasta'], '.pdb')
sc_df = Rf.score_file2df(args['sc'])
best_by_aa_freq_df = Rf.get_best_num_by_term(sc_df, 10, 'a_tms_aa_comp')
names_to_use = list(best_by_aa_freq_df['description'])
names_to_use += [args['sc'].split('.')[0].split('all_')[1]]
all_seqs = {k: v for k, v in all_seqs.items() if k in names_to_use}
seqs_aa_freqs = {n: {} for n in all_seqs.keys() if n in names_to_use}
freq_dists = {}
for n, s in all_seqs.items():
seqs_aa_freqs[n] = s.all_aas_frequencies(clean_zeros=True)
# freq_dists[n] = calc_freq_dist(seqs_aa_freqs[n], wt_aa_freq,
# 'AGFILPSTVWY')
freq_dists[n] = calc_freq_dist(seqs_aa_freqs[n], wt_aa_freq)
freq_dists = OrderedDict(sorted(freq_dists.items(), key=lambda t: t[1]))
fig = plt.figure(figsize=(18, 18))
nrows = math.floor(len(all_seqs.keys()) / 4)
nrows += 1 if len(all_seqs.keys()) % 4 > 0 else 0
i = 0
# for n, s in all_seqs.items():
first = True
for n, dist_freq in freq_dists.items():
if first:
print(n)
first = False
s = all_seqs[n]
plt.subplot(nrows, 4, 1 + i)
fracs = [a for a in seqs_aa_freqs[n].values()]
labels = [a for a in seqs_aa_freqs[n].keys()]
colors = [color_map[a] for a in labels]
plt.pie(fracs, labels=labels, autopct='%1.1f%%', colors=colors)
name = n.split('.pdb')[0]
if name.count('poly') == 2:
p_name = name.split('_poly')[0]
else:
p_name = name
if name in list(sc_df['description']):
title = (
'%s\nscore: %.0f ∆∆G: %.0f\nfreq_dist: %.2f\naa_comp %.2f' %
(p_name, sc_df[sc_df['description'] == name]['score'],
sc_df[sc_df['description'] == name]['a_ddg'], dist_freq * 100,
best_by_aa_freq_df[best_by_aa_freq_df['description'] ==
name]['a_tms_aa_comp']))
else:
title = 'freq_dist %.2f' % (dist_freq * 100)
plt.title(title)
i += 1
if args['show'] == 'show':
plt.show()
else:
plt.savefig('%s_aa_freq.png' % args['fasta'])
def meta_pie(args: dict):
mut_aa_freqs = {aa: 0 for aa in aas}
score_files = [a for a in os.listdir(args['dir']) if '.score' in a]
for fasta_file in [a for a in os.listdir(args['dir']) if '.fasta' in a]:
temp_seqs = read_seqs('%s/%s' % (args['dir'], fasta_file),
remove_suffix='.pdb')
temp_sc = Rf.score_file2df('%s/%s.score' %
(args['dir'], fasta_file.split('.')[0]))
names = list(
Rf.get_best_num_by_term(temp_sc, 10,
'a_tms_aa_comp')['description'])
for n, aaseq in temp_seqs.items():
if n in names:
for aa in aas:
mut_aa_freqs[aa] += (aaseq.aa_frequency(aa) * len(aaseq))
mut_aa_freqs_srt = OrderedDict(
sorted(mut_aa_freqs.items(), key=lambda t: t[1]))
ori_aa_freqs = {aa: 0 for aa in aas}
for aaseq in read_seqs('/home/labs/fleishman/jonathaw/elazaridis/design/' +
'polyA_13Nov/chosen_from_all_27Feb/pdbs/' +
'all_dzns.fasta').values():
for aa in aas:
ori_aa_freqs[aa] += (aaseq.aa_frequency(aa) * len(aaseq))
ori_aa_freqs_srt = OrderedDict(
sorted(ori_aa_freqs.items(), key=lambda t: t[1]))
plt.figure()
plt.subplot(1, 3, 1)
plt.title('natural TMs')
plt.pie(list(wt_aa_freq.values()),
labels=list(wt_aa_freq.keys()),
autopct='%1.1f%%',
colors=[color_map[a] for a in list(wt_aa_freq.keys())])
plt.axis('equal')
plt.subplot(1, 3, 2)
plt.title('original designs')
plt.pie(list(ori_aa_freqs_srt.values()),
labels=list(ori_aa_freqs_srt.keys()),
autopct='%1.1f%%',
colors=[color_map[a] for a in list(ori_aa_freqs_srt.keys())])
plt.axis('equal')
plt.subplot(1, 3, 3)
plt.title('mutated designs')
plt.pie(list(mut_aa_freqs_srt.values()),
labels=list(mut_aa_freqs_srt.keys()),
autopct='%1.1f%%',
colors=[color_map[a] for a in list(mut_aa_freqs_srt.keys())])
plt.axis('equal')
plt.show()
