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| import pandas as pd | |
| import numpy as np | |
| import glob | |
| import random | |
| import numpy as np | |
| import json | |
| def softmax(x, T): | |
| return np.exp(x/T)/np.sum(np.exp(x/T), -1, keepdims=True) | |
| def parse_pssm(path): | |
| data = pd.read_csv(path, skiprows=2) | |
| floats_list_list = [] | |
| for i in range(data.values.shape[0]): | |
| str1 = data.values[i][0][4:] | |
| floats_list = [] | |
| for item in str1.split(): | |
| floats_list.append(float(item)) | |
| floats_list_list.append(floats_list) | |
| np_lines = np.array(floats_list_list) | |
| return np_lines | |
| np_lines = parse_pssm('/home/swang523/RLcage/capsid/monomersfordesign/8-16-21/pssm_rainity_final_8-16-21_int/build_0.2089_0.98_0.4653_19_2.00_0.005745.pssm') | |
| mpnn_alphabet = 'ACDEFGHIKLMNPQRSTVWYX' | |
| input_alphabet = 'ARNDCQEGHILKMFPSTWYV' | |
| permutation_matrix = np.zeros([20,21]) | |
| for i in range(20): | |
| letter1 = input_alphabet[i] | |
| for j in range(21): | |
| letter2 = mpnn_alphabet[j] | |
| if letter1 == letter2: | |
| permutation_matrix[i,j]=1. | |
| pssm_log_odds = np_lines[:,:20] @ permutation_matrix | |
| pssm_probs = np_lines[:,20:40] @ permutation_matrix | |
| X_mask = np.concatenate([np.zeros([1,20]), np.ones([1,1])], -1) | |
| def softmax(x, T): | |
| return np.exp(x/T)/np.sum(np.exp(x/T), -1, keepdims=True) | |
| #Load parsed PDBs: | |
| with open('/home/justas/projects/cages/parsed/test.jsonl', 'r') as json_file: | |
| json_list = list(json_file) | |
| my_dict = {} | |
| for json_str in json_list: | |
| result = json.loads(json_str) | |
| all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] | |
| pssm_dict = {} | |
| for chain in all_chain_list: | |
| pssm_dict[chain] = {} | |
| pssm_dict[chain]['pssm_coef'] = (np.ones(len(result['seq_chain_A']))).tolist() #a number between 0.0 and 1.0 specifying how much attention put to PSSM, can be adjusted later as a flag | |
| pssm_dict[chain]['pssm_bias'] = (softmax(pssm_log_odds-X_mask*1e8, 1.0)).tolist() #PSSM like, [length, 21] such that sum over the last dimension adds up to 1.0 | |
| pssm_dict[chain]['pssm_log_odds'] = (pssm_log_odds).tolist() | |
| my_dict[result['name']] = pssm_dict | |
| #Write output to: | |
| with open('/home/justas/projects/lab_github/mpnn/data/pssm_dict.jsonl', 'w') as f: | |
| f.write(json.dumps(my_dict) + '\n') | |