def get_mixed_entropy_values(PDB, buried_temp, surface_temp):
new_entropies = []
#Make the files
buried_file = "align_data_array_" + PDB + "_" + str(buried_temp) + ".dat"
surface_file = "align_data_array_" + PDB + "_" + str(surface_temp) + ".dat"
#Get the RSA Values
RSA = af.make_array(af.get_RSA_Values(buried_file))
buried_entropies = af.get_native_entropy(buried_file)
surface_entropies = af.get_native_entropy(surface_file)
#Get the entropy values
for i in xrange(len(RSA)):
if (float(RSA[i]) <=0.25):
new_entropies.append(buried_entropies[i])
else:
new_entropies.append(surface_entropies[i])
return RSA, new_entropies
pdb_names.append(pdb_id)
chain_names.append(chain_id)
natural_proteins = file #Open the files with results
designed_proteins_rosetta = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(
"rosetta") + ".dat"
designed_proteins_evolved = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(
"evolved") + ".dat"
split_natural_1 = "align_natural_sample1_data_array_" + pdb_id + "_" + chain_id + ".dat"
split_natural_2 = "align_natural_sample2_data_array_" + pdb_id + "_" + chain_id + ".dat"
#Calculates all of the data for comparison (ex. entropy)
natural_distribution = analysis_functions.get_AA_distribution_KL(
natural_proteins)
natural_entropy = analysis_functions.get_native_entropy(
natural_proteins)
natural_entropy_array = analysis_functions.make_array(
natural_entropy)
natural_RSA = analysis_functions.get_RSA_Values(natural_proteins)
natural_RSA_array = analysis_functions.make_array(natural_RSA)
natural_mean_RSA_values.append(mean(natural_RSA_array))
natural_mean_entropy_values.append(mean(natural_entropy_array))
#Calculates cn & wcn
# cn13_data = analysis_functions.get_cn13_values(pdb_id, chain_id)
# iCN13 = cn13_data[0]
# iCN13_array = analysis_functions.make_array(cn13_data)
# mean_iCN13_values.append(mean(iCN13_array))
iwcn_data = calc_wcn.get_iwcn_values(pdb_id, chain_id)
iWCN_array = analysis_functions.make_array(iwcn_data)
for PDB in PDBS:
RSA1, entropy_mix1 = get_mixed_entropy_values(PDB, 0.0, 0.1)
RSA2, entropy_mix2 = get_mixed_entropy_values(PDB, 0.03, 0.1)
[cor_entropy_RSA_mix1, pvalue1] = pearsonr(RSA1, entropy_mix1)
cor_entropy_RSA_mix1 = float(cor_entropy_RSA_mix1)
cor_values1.append(cor_entropy_RSA_mix1)
[cor_entropy_RSA_mix2, pvalue2] = pearsonr(RSA2, entropy_mix2)
cor_entropy_RSA_mix2 = float(cor_entropy_RSA_mix2)
cor_values2.append(cor_entropy_RSA_mix2)
natural_file = "align_natural_data_array_" + PDB + ".dat"
natural_RSA = af.make_array(af.get_RSA_Values(natural_file))
natural_entropy = af.get_native_entropy(natural_file)
[natural_cor_entropy_RSA, pvalue3] = pearsonr(natural_RSA, natural_entropy)
natural_cor_entropy_RSA = float(natural_cor_entropy_RSA)
natural_cor_values.append(natural_cor_entropy_RSA)
fig = plt.figure(1, dpi = 400, figsize = (16,6))
correlation_values = [cor_values1, cor_values2, natural_cor_values]
correlation_values_transpose = transpose(correlation_values)
(m,n) = correlation_values_transpose.shape
#rcParams['lines.linewidth'] = 2
ax = axes([0.066, 0.115, 0.43, 0.85])
#text(-0.37, 0.6, "A", fontweight = 'bold', ha = 'center', va = 'center', fontsize = 20)
'''
b1 = boxplot(correlation_values, sym = 'ko')
natural_proteins = file #Open the files with results designed_proteins_00 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(0.0) + ".dat" designed_proteins_01 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(0.1) + ".dat" designed_proteins_03 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(0.3) + ".dat" designed_proteins_06 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(0.6) + ".dat" designed_proteins_09 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(0.9) + ".dat" designed_proteins_12 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(1.2) + ".dat" designed_proteins_003 = "align_data_array_" + pdb_id + "_" + chain_id + "_" + str(0.03) + ".dat" split_natural_1 = "align_natural_sample1_data_array_" + pdb_id + "_" + chain_id + ".dat" split_natural_2 = "align_natural_sample2_data_array_" + pdb_id + "_" + chain_id + ".dat" #Calculates all of the data for comparison (ex. entropy) natural_distribution = analysis_functions.get_AA_distribution(natural_proteins) natural_entropy = analysis_functions.get_native_entropy(natural_proteins) natural_entropy_array = analysis_functions.make_array(natural_entropy) natural_RSA = analysis_functions.get_RSA_Values(natural_proteins) natural_RSA_array = analysis_functions.make_array(natural_RSA) natural_mean_RSA_values.append(mean(natural_RSA_array)) natural_mean_entropy_values.append(mean(natural_entropy_array)) designed_distribution_00 = analysis_functions.get_AA_distribution(designed_proteins_00) designed_entropy_00 = analysis_functions.get_native_entropy(designed_proteins_00) designed_entropy_array_00 = analysis_functions.make_array(designed_entropy_00) designed_RSA_00 = analysis_functions.get_RSA_Values(designed_proteins_00) designed_RSA_array_00 = analysis_functions.make_array(designed_RSA_00) designed_mean_RSA_values_00.append(mean(designed_RSA_array_00)) designed_mean_entropy_values_00.append(mean(designed_entropy_array_00))
