| |
| import pickle |
| import numpy as np |
| import torch |
| from tqdm import tqdm |
| from pad_feature import parse_fasta_file |
| from get_dssp import get_dssp |
| from get_SC_position import PDBFeature |
| from get_SC_adj import prepare_adj |
| from get_T5embedding import getT5 |
| from pad_feature import prepare_features |
|
|
|
|
|
|
| def process(dataset): |
| |
| getT5(dataset,'./T5raw/','0') |
|
|
| data = parse_fasta_file(dataset) |
| Max_protrans = [] |
| Min_protrans = [] |
| for i, ID in tqdm(enumerate(data.keys())): |
| raw_protrans = np.load('./T5raw/' + ID + ".npy") |
| Max_protrans.append(np.max(raw_protrans, axis = 0)) |
| Min_protrans.append(np.min(raw_protrans, axis = 0)) |
| if i == len(data) - 1: |
| Max_protrans = np.max(np.array(Max_protrans), axis = 0) |
| Min_protrans = np.min(np.array(Min_protrans), axis = 0) |
| elif i % 5000 == 0: |
| Max_protrans = [np.max(np.array(Max_protrans), axis = 0)] |
| Min_protrans = [np.min(np.array(Min_protrans), axis = 0)] |
|
|
|
|
| for ID in tqdm(data.keys()): |
| |
| raw_protrans = np.load('./T5raw/' + ID + ".npy") |
| |
| protrans = (raw_protrans - Min_protrans) / (Max_protrans - Min_protrans) |
| |
| np.save('./T5norm/' + ID + '.npy', protrans) |
|
|
| |
| get_dssp(ID, data[ID][0]) |
|
|
| |
| PDBFeature(ID, '../datasets/alphafold3pdb', './SC_position') |
|
|
| |
| prepare_adj(ID,869) |
|
|
| |
| prepare_features(ID, data[ID][1], 869) |
|
|
| if __name__ == '__main__': |
| fasta_file = '../datasets/PRO_Train_335.fa' |
| process(fasta_file) |
| fasta_file = '../datasets/PRO_Test_60.fa' |
| process(fasta_file) |
|
|
