Feel free to contact me via email at yuhanid147@gmail.com if you encounter any issues or have any questions.
Introduction of UniKP.
Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.
Here is the framework of UniKP.
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For users who want to know what to expect in this project, as follows:
- (1). Out the kcat values given protein sequences and substrate structures.
- (2). Out the Km values given protein sequences and substrate structures.
- (3). Out the kcat / Km values given protein sequences and substrate structures.
| Input_v1 | Input_v2 | Model | Output |
|---|---|---|---|
| MSELMKLSAV...MAQR | CC(O)O | UniKP for kcat | 2.75 s-1 |
| MSELMKLSAV...MAQR | CC(O)O | UniKP for Km | 0.36 mM |
| MSELMKLSAV...MAQR | CC(O)O | UniKP for kcat / Km | 9.51 s-1 * mM-1 |
Notice:
- You need download pretrained protein language modoel ProtT5-XL-UniRef50 to generate enzyme representation, the link is provided on ProtT5-XL-U50.
- You also need download model UniKP for kcat, Km and kcat / Km to predict corresponding kinetic parameters, the link is provided on UniKP_model.
Place these two downloaded models in the UniKP directory.
- We have included pretrained molecular language modoel SMILES Transformer in this repository to generate substrate representation, the link is also provided on SMILES Transformer.
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For users who want to use the deep learning model for prediction, please run these command lines at the terminal:
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(1). Download the UniKP package
git clone https://github.com/Luo-SynBioLab/UniKP -
(2). Create and activate enviroment
conda create -n Uni_test python=3.7 conda activate Uni_test -
(3). Download required Python package
cd UniKP pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu113 pip install -r requirements.txt
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Example for how to predict enzyme kinetic parameters from enzyme sequences and substrate structures by language model, UniKP:
All predicted values have been logarithmically transformed with a base of 10. Remember to revert the transformation.
import torch
from build_vocab import WordVocab
from pretrain_trfm import TrfmSeq2seq
from utils import split
# build_vocab, pretrain_trfm, utils packages are from SMILES Transformer
from transformers import T5EncoderModel, T5Tokenizer
# transformers package is from ProtTrans
import re
import gc
import numpy as np
import pandas as pd
import pickle
import math
def smiles_to_vec(Smiles):
pad_index = 0
unk_index = 1
eos_index = 2
sos_index = 3
mask_index = 4
vocab = WordVocab.load_vocab('vocab.pkl')
def get_inputs(sm):
seq_len = 220
sm = sm.split()
if len(sm)>218:
print('SMILES is too long ({:d})'.format(len(sm)))
sm = sm[:109]+sm[-109:]
ids = [vocab.stoi.get(token, unk_index) for token in sm]
ids = [sos_index] + ids + [eos_index]
seg = [1]*len(ids)
padding = [pad_index]*(seq_len - len(ids))
ids.extend(padding), seg.extend(padding)
return ids, seg
def get_array(smiles):
x_id, x_seg = [], []
for sm in smiles:
a,b = get_inputs(sm)
x_id.append(a)
x_seg.append(b)
return torch.tensor(x_id), torch.tensor(x_seg)
trfm = TrfmSeq2seq(len(vocab), 256, len(vocab), 4)
trfm.load_state_dict(torch.load('trfm_12_23000.pkl'))
trfm.eval()
x_split = [split(sm) for sm in Smiles]
xid, xseg = get_array(x_split)
X = trfm.encode(torch.t(xid))
return X
def Seq_to_vec(Sequence):
for i in range(len(Sequence)):
if len(Sequence[i]) > 1000:
Sequence[i] = Sequence[i][:500] + Sequence[i][-500:]
sequences_Example = []
for i in range(len(Sequence)):
zj = ''
for j in range(len(Sequence[i]) - 1):
zj += Sequence[i][j] + ' '
zj += Sequence[i][-1]
sequences_Example.append(zj)
###### you should place downloaded model into this directory.
tokenizer = T5Tokenizer.from_pretrained("prot_t5_xl_uniref50", do_lower_case=False)
model = T5EncoderModel.from_pretrained("prot_t5_xl_uniref50")
gc.collect()
print(torch.cuda.is_available())
# 'cuda:0' if torch.cuda.is_available() else
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model = model.eval()
features = []
for i in range(len(sequences_Example)):
print('For sequence ', str(i+1))
sequences_Example_i = sequences_Example[i]
sequences_Example_i = [re.sub(r"[UZOB]", "X", sequences_Example_i)]
ids = tokenizer.batch_encode_plus(sequences_Example_i, add_special_tokens=True, padding=True)
input_ids = torch.tensor(ids['input_ids']).to(device)
attention_mask = torch.tensor(ids['attention_mask']).to(device)
with torch.no_grad():
embedding = model(input_ids=input_ids, attention_mask=attention_mask)
embedding = embedding.last_hidden_state.cpu().numpy()
for seq_num in range(len(embedding)):
seq_len = (attention_mask[seq_num] == 1).sum()
seq_emd = embedding[seq_num][:seq_len - 1]
features.append(seq_emd)
features_normalize = np.zeros([len(features), len(features[0][0])], dtype=float)
for i in range(len(features)):
for k in range(len(features[0][0])):
for j in range(len(features[i])):
features_normalize[i][k] += features[i][j][k]
features_normalize[i][k] /= len(features[i])
return features_normalize
if __name__ == '__main__':
sequences = ['MEDIPDTSRPPLKYVKGIPLIKYFAEALESLQDFQAQPDDLLISTYPKSGTTWVSEILDMIYQDGDVEKCRRAPVFIRVPFLEFKA'
'PGIPTGLEVLKDTPAPRLIKTHLPLALLPQTLLDQKVKVVYVARNAKDVAVSYYHFYRMAKVHPDPDTWDSFLEKFMAGEVSYGSW'
'YQHVQEWWELSHTHPVLYLFYEDMKENPKREIQKILKFVGRSLPEETVDLIVQHTSFKEMKNNSMANYTTLSPDIMDHSISAFMRK'
'GISGDWKTTFTVAQNERFDADYAKKMEGCGLSFRTQL']
Smiles = ['OC1=CC=C(C[C@@H](C(O)=O)N)C=C1']
seq_vec = Seq_to_vec(sequences)
smiles_vec = smiles_to_vec(Smiles)
fused_vector = np.concatenate((smiles_vec, seq_vec), axis=1)
###### you should place downloaded model into this directory.
# For kcat
with open('UniKP/UniKP for kcat.pkl', "rb") as f:
model = pickle.load(f)
# For Km
# with open('UniKP/UniKP for Km.pkl', "rb") as f:
# model = pickle.load(f)
# For kcat/Km
# with open('UniKP/UniKP for kcat_Km.pkl', "rb") as f:
# model = pickle.load(f)
Pre_label = model.predict(fused_vector)
Pre_label_pow = [math.pow(10, Pre_label[i]) for i in range(len(Pre_label))]
print(len(Pre_label_pow))
res = pd.DataFrame({'sequences': sequences, 'Smiles': Smiles, 'Pre_label': Pre_label_pow})
res.to_excel('Kinetic_parameters_predicted_label.xlsx')
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China:
| Han Yu | Huaxiang Deng | Jiahui He | Jay D. Keasling | Xiaozhou Luo |
|---|
We would like to acknowledge the support from National Key R&D Program of China (2018YFA0903200), National Natural Science Foundation of China (32071421), Guangdong Basic and Applied Basic Research Foundation (2021B1515020049), Shenzhen Science and Technology Program (ZDSYS20210623091810032 and JCYJ20220531100207017), and Shenzhen Institute of Synthetic Biology Scientific Research Program (ZTXM20203001).
GNU General Public License version 3
If you use this code or our models for your publication, please cite the original paper:
Yu, H., Deng, H., He, J. et al. UniKP: a unified framework for the prediction of enzyme kinetic parameters. Nat Commun 14, 8211 (2023). [https://doi.org/10.1038/s41467-023-44113-1]
The preprint version:
Han Yu, Huaxiang Deng, Jiahui He et al. Highly accurate enzyme turnover number prediction and enzyme engineering with PreKcat, 18 May 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2749688/v1]