# UniKP

**Repository Path**: william2015c/UniKP

## Basic Information

- **Project Name**: UniKP
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-25
- **Last Updated**: 2024-11-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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# UniKP

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**Introduction of UniKP.**

Accurate enzyme kinetic parameters prediction enables optimization of reaction conditions, increasing catalytic efficiency, yield and specificity, and improving overall process economics in enzyme-dependent industries such as pharmaceuticals, food, and chemicals.The lack of a unified framework and reliable prediction tools capable of handling diverse tasks limits their practical application in numerous fields, where accurate predictions of enzyme properties can significantly impact research outcomes.Here, we introduced UniKP, a unified framework based on pretrained language models for highly accurate enzyme ***k*<sub>cat</sub>, *K*<sub>m</sub>** and ***k*<sub>cat</sub> / *K*<sub>m</sub>** prediction from protein sequences and substrate structures.

**Here is the framework of UniKP.**
<p align="center">
  <img  src="Figures/UniKP.jpg" >
</p>

# Demo-Preview

- **For users who want to know what to expect in this project, as follows:**

  - (1). Out the *k*<sub>cat</sub> values given protein sequences and substrate structures.
  - (2). Out the *K*<sub>m</sub> values given protein sequences and substrate structures.
  - (3). Out the *k*<sub>cat</sub> / *K*<sub>m</sub> values given protein sequences and substrate structures.

|Input_v1|Input_v2|Model|Output|
|--|--|--|--|
| MSELMKLSAV...MAQR | CC(O)O | UniKP for *k*<sub>cat</sub> | 2.75 s<sup>-1</sup> |
| MSELMKLSAV...MAQR | CC(O)O | UniKP for *K*<sub>m</sub> |  0.36 mM |
| MSELMKLSAV...MAQR | CC(O)O | UniKP for *k*<sub>cat</sub> / *K*<sub>m</sub> |  9.51 s<sup>-1</sup> * mM<sup>-1</sup> |

# Table of contents

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- [UniKP](#unikp)
- [Demo-Preview](#demo-preview)
- [Table of contents](#table-of-contents)
- [Installation](#installation)
- [Usage](#usage)
- [Development](#development)
- [Contribute](#contribute)
    - [Sponsor](#sponsor)
    - [Adding new features or fixing bugs](#adding-new-features-or-fixing-bugs)
- [License](#license)
- [Footer](#footer)

# Installation
[(Back to top)](#table-of-contents)

Notice:
- You need install pretrained protein language modoel *ProtT5-XL-UniRef50* to generate enzyme representation, the link is provided on [ProtT5-XL-U50](https://github.com/agemagician/ProtTrans#models).
- You need install pretrained molecular language modoel *SMILES Transformer* to generate substrate representation, the link is provided on [SMILES Transformer](https://github.com/DSPsleeporg/smiles-transformer).
- You also need install model *UniKP* for ***k*<sub>cat</sub>, *K*<sub>m</sub>** and ***k*<sub>cat</sub> / *K*<sub>m</sub>** to predict corresponding kinetic parameters, the link is provided on [UniKP_model](https://huggingface.co/HanselYu/UniKP).

other packages:
- Python v3.6.9 (Anaconda installation recommended)
- PyTorch v1.10.1+cu113
- pandas v1.1.5
- NumPy v1.19.5

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# Usage
[(Back to top)](#table-of-contents)
- **For users who want to use the deep learning model for prediction, please run these command lines at the terminal:**
  - (1). Download the UniKP package
  
         git clone https://github.com/Luo-SynBioLab/UniKP
  
  - (2). Download required Python package
  
         pip install numpy pandas PyTorch v1.10.1+cu113
 

- Example for how to predict enzyme kinetic parameters from enzyme sequences and substrate structures by language model, UniKP:
```
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('UniKP/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('UniKP/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)
    tokenizer = T5Tokenizer.from_pretrained("UniKP/prot_t5_xl_uniref50", do_lower_case=False)
    model = T5EncoderModel.from_pretrained("UniKP/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)
    
    # 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))
    res = pd.DataFrame({'sequences': sequences, 'Smiles': Smiles, 'Pre_label': Pre_label})
    res.to_excel('Kinetic_parameters_predicted_label.xlsx')
```

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# Development
[(Back to top)](#table-of-contents)

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# Contribute
[(Back to top)](#table-of-contents)

 * <b>Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China:</b><br/>
 
| Han Yu       |      Huaxiang Deng  |  Jiahui He| Jay D. Keasling | Xiaozhou Luo |
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
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### Sponsor
[(Back to top)](#table-of-contents)

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).

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# License
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[GNU General Public License version 3](https://opensource.org/licenses/GPL-3.0)

# Footer
[(Back to top)](#table-of-contents)

If you use this code or our models for your publication, please cite the original paper:

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]

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