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))