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main.py
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import os
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
def get_unnorm_asa_new(rel_asa, seq):
"""
:param asa_pred: The predicted relative ASA
:param seq_list: Sequence of the protein
:return: absolute ASA_PRED
"""
rnam1_std = "ACDEFGHIKLMNPQRSTVWY-X"
ASA_std = (115, 135, 150, 190, 210, 75, 195, 175, 200, 170,
185, 160, 145, 180, 225, 115, 140, 155, 255, 230, 1, 1)
dict_rnam1_ASA = dict(zip(rnam1_std, ASA_std))
max_seq_len = len(seq[0])
array_list = []
for i, single_seq in enumerate(list(seq)):
rel_asa_current = rel_asa[i, :]
seq_len_diff = max_seq_len - len(single_seq)
single_seq = single_seq + ("X" * seq_len_diff)
asa_max = np.array([dict_rnam1_ASA[i] for i in single_seq]).astype(np.float32)
abs_asa = np.multiply(rel_asa_current.cpu().detach().numpy(), asa_max)
array_list.append(abs_asa)
final_array = np.array(array_list)
return final_array
def get_angle_degree(preds):
preds = preds * 2 - 1
preds_sin = preds[:, :, 0]
preds_cos = preds[:, :, 1]
preds_angle_rad = np.arctan2(preds_sin, preds_cos)
preds_angle = np.degrees(preds_angle_rad)
return preds_angle
ss_conv_3_8_dict = {'X': 'X', 'C': 'C', 'S': 'C', 'T': 'C', 'H': 'H', 'G': 'H', 'I': 'H', 'E': 'E', 'B': 'E'}
SS3_CLASSES = 'CEH'
SS8_CLASSES = 'CSTHGIEB'
def main_reg(data_loader, model1, model2, model3, device):
psi_list = []
phi_list = []
theta_list = []
tau_list = []
hseu_list = []
hsed_list = []
cn_list = []
asa_list = []
model1.eval()
model2.eval()
model3.eval()
for i, data in enumerate(tqdm(data_loader)):
feats, length, name, seq = data
feats = feats.to(device, dtype=torch.float)
pred1 = model1(feats, length)
pred2 = model2(feats, length)
pred3 = model3(feats, length)
psi_pred1 = pred1[:, :, 0:2].unsqueeze(3)
psi_pred2 = pred2[:, :, 0:2].unsqueeze(3)
psi_pred3 = pred3[:, :, 0:2].unsqueeze(3)
psi_pred_cat = torch.cat((psi_pred1, psi_pred2, psi_pred3), 3)
psi_pred, _ = torch.median(psi_pred_cat, dim=-1)
psi_deg = get_angle_degree(psi_pred.cpu().detach().numpy())
for i, len_prot in enumerate(list(length)):
psi_list.append(psi_deg[i, :int(len_prot), None])
phi_pred1 = pred1[:, :, 2:4].unsqueeze(3)
phi_pred2 = pred2[:, :, 2:4].unsqueeze(3)
phi_pred3 = pred3[:, :, 2:4].unsqueeze(3)
phi_pred_cat = torch.cat((phi_pred1, phi_pred2, phi_pred3), 3)
phi_pred, _ = torch.median(phi_pred_cat, dim=-1)
phi_deg = get_angle_degree(phi_pred.cpu().detach().numpy())
for i, len_prot in enumerate(list(length)):
phi_list.append(phi_deg[i, :int(len_prot), None])
theta_pred1 = pred1[:, :, 4:6].unsqueeze(3)
theta_pred2 = pred2[:, :, 4:6].unsqueeze(3)
theta_pred3 = pred3[:, :, 4:6].unsqueeze(3)
theta_pred_cat = torch.cat((theta_pred1, theta_pred2, theta_pred3), 3)
theta_pred, _ = torch.median(theta_pred_cat, dim=-1)
theta_deg = get_angle_degree(theta_pred.cpu().detach().numpy())
for i, len_prot in enumerate(list(length)):
theta_list.append(theta_deg[i, :int(len_prot), None])
tau_pred1 = pred1[:, :, 6:8].unsqueeze(3)
tau_pred2 = pred2[:, :, 6:8].unsqueeze(3)
tau_pred3 = pred3[:, :, 6:8].unsqueeze(3)
tau_pred_cat = torch.cat((tau_pred1, tau_pred2, tau_pred3), 3)
tau_pred, _ = torch.median(tau_pred_cat, dim=-1)
tau_deg = get_angle_degree(tau_pred.cpu().detach().numpy())
# tau_list = [tau_deg[i, :int(len_prot), None] for i, len_prot in enumerate(list(length))]
for i, len_prot in enumerate(list(length)):
tau_list.append(tau_deg[i, :int(len_prot), None])
hseu_pred1 = pred1[:, :, 8]
hseu_pred2 = pred2[:, :, 8]
hseu_pred3 = pred3[:, :, 8]
hseu_pred = ((hseu_pred1 + hseu_pred2 + hseu_pred3) / 3) * 50
hseu_pred = hseu_pred.cpu().detach().numpy()
for i, len_prot in enumerate(list(length)):
hseu_list.append(hseu_pred[i, :int(len_prot), None])
hsed_pred1 = pred1[:, :, 9]
hsed_pred2 = pred2[:, :, 9]
hsed_pred3 = pred3[:, :, 9]
hsed_pred = ((hsed_pred1 + hsed_pred2 + hsed_pred3) / 3) * 65
hsed_pred = hsed_pred.cpu().detach().numpy()
for i, len_prot in enumerate(list(length)):
hsed_list.append(hsed_pred[i, :int(len_prot), None])
cn_pred1 = pred1[:, :, 10]
cn_pred2 = pred2[:, :, 10]
cn_pred3 = pred3[:, :, 10]
cn_pred = ((cn_pred1 + cn_pred2 + cn_pred3) / 3) * 85
cn_pred = cn_pred.cpu().detach().numpy()
for i, len_prot in enumerate(list(length)):
cn_list.append(cn_pred[i, :int(len_prot), None])
asa_pred1 = pred1[:, :, -1]
asa_pred2 = pred2[:, :, -1]
asa_pred3 = pred3[:, :, -1]
asa_pred = (asa_pred1 + asa_pred2 + asa_pred3) / 3
asa_pred = get_unnorm_asa_new(asa_pred, seq)
asa_pred = asa_pred
for i, len_prot in enumerate(list(length)):
asa_list.append(asa_pred[i, :int(len_prot), None])
return psi_list, phi_list, theta_list, tau_list, hseu_list, hsed_list, cn_list, asa_list
def main_class(data_loader, model1, model2, model3, device):
ss3_pred_list = []
ss8_pred_list = []
ss3_prob_list = []
ss8_prob_list = []
names_list = []
seq_list = []
model1.eval()
model2.eval()
model3.eval()
for i, data in enumerate(tqdm(data_loader)):
feats, length, name, seq = data
feats = feats.to(device, dtype=torch.float)
pred1 = model1(feats, length)
pred2 = model2(feats, length)
pred3 = model3(feats, length)
pred = (pred1 + pred2 + pred3) / 3
pred = pred.view(-1, 11)
ss3_pred = pred[:, 0:3]
ss8_pred = pred[:, 3:]
name = list(name)
for i, prot_len in enumerate(list(length)):
prot_len_int = int(prot_len)
ss3_pred_single = ss3_pred[:prot_len_int, :]
ss3_pred_single = ss3_pred_single.cpu().detach().numpy()
ss3_indices = np.argmax(ss3_pred_single, axis=1)
ss3_pred_aa = np.array([SS3_CLASSES[aa] for aa in ss3_indices])[:, None]
ss3_pred_list.append(ss3_pred_aa)
ss3_prob_list.append(ss3_pred_single)
ss8_pred_single = ss8_pred[:prot_len_int, :]
ss8_pred_single = ss8_pred_single.cpu().detach().numpy()
ss8_indices = np.argmax(ss8_pred_single, axis=1)
ss8_pred_aa = np.array([SS8_CLASSES[aa] for aa in ss8_indices])[:, None]
ss8_pred_list.append(ss8_pred_aa)
ss8_prob_list.append(ss8_pred_single)
names_list.append(name[i])
for seq in list(seq):
seq_list.append(np.array([i for i in seq])[:, None])
return names_list, seq_list, ss3_pred_list, ss8_pred_list, ss3_prob_list, ss8_prob_list
def write_csv(class_out, reg_out, save_dir):
names, seq, ss3_pred_list, ss8_pred_list, ss3_prob_list, ss8_prob_list = class_out
psi_list, phi_list, theta_list, tau_list, hseu_list, hsed_list, cn_list, asa_list = reg_out
for seq, ss3, ss8, asa, hseu, hsed, cn, psi, phi, theta, tau, ss3_prob, ss8_prob, name in zip(seq, ss3_pred_list,
ss8_pred_list,
asa_list, hseu_list,
hsed_list, cn_list,
psi_list, phi_list,
theta_list, tau_list,
ss3_prob_list,
ss8_prob_list, names):
data = np.concatenate((seq, ss3, ss8, asa, hseu, hsed, cn, psi, phi, theta, tau, ss3_prob, ss8_prob), axis=1)
save_path = os.path.join(save_dir, name + ".csv")
pd.DataFrame(data).to_csv(save_path,
header=["AA", "SS3", "SS8", "ASA", "HseU", "HseD", "CN", "Psi", "Phi", "Theta",
"Tau", "P3C", "P3E", "P3H", "P8C", "P8S", "P8T", "P8H", "P8G",
"P8I", "P8E", "P8B"])
return print(f'please find the results saved at {save_dir} with .csv extention')
if __name__ == '__main__':
print("Please run the run_SPOT-1D-LM.sh instead")