https://github.com/sysbiochalmers/dlkcat

Deep learning and Bayesian approach applied to enzyme turnover number for the improvement of enzyme-constrained genome-scale metabolic models (ecGEMs) reconstruction
https://github.com/sysbiochalmers/dlkcat

bayesian deep-learning enzyme-constraints enzyme-turnover-number kcat kinetics

Last synced: about 1 month ago
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Deep learning and Bayesian approach applied to enzyme turnover number for the improvement of enzyme-constrained genome-scale metabolic models (ecGEMs) reconstruction

• Host: GitHub
• URL: https://github.com/sysbiochalmers/dlkcat
• Owner: SysBioChalmers
• Created: 2020-05-19T09:43:48.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-12-21T14:38:00.000Z (about 2 years ago)
• Last Synced: 2023-06-05T04:26:32.688Z (over 1 year ago)
• Topics: bayesian, deep-learning, enzyme-constraints, enzyme-turnover-number, kcat, kinetics
• Language: Python
• Homepage:
• Size: 46.2 MB
• Stars: 63
• Watchers: 5
• Forks: 36
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
DLKcat

======



  



Introduction

------------

The **DLKcat** toolbox is a Matlab/Python package for prediction of

kcats and generation of the ecGEMs. The repo is divided into two parts:

`DeeplearningApproach` and `BayesianApproach`. `DeeplearningApproach`

supplies a deep-learning based prediction tool for kcat prediction,

while `BayesianApproach` supplies an automatic Bayesian based pipeline

to construct ecModels using the predicted kcats.

Usage

-----

-   Please check the instruction `README` file under these two section

    `Bayesianapproach` and `DeeplearningApproach` for reporducing all figures in

    the paper.

-   For people who are interested in using the trained deep-learning

    model for their own kcat prediction, we supplied an example. please

    check usage for **detailed information** in the file

    [DeeplearningApproach/README](https://github.com/SysBioChalmers/DLKcat/tree/master/DeeplearningApproach)

    under the `DeeplearningApproach`.

    > -   `input` for the prediction is the `Protein sequence` and

    >     `Substrate SMILES structure/Substrate name`, please check the

    >     file in

    >     [DeeplearningApproach/Code/example/input.tsv](https://github.com/SysBioChalmers/DLKcat/tree/master/DeeplearningApproach/Code/example)

    > -   `output` is the correponding `kcat` value

Citation

-----

- Please cite the paper [Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction](https://www.nature.com/articles/s41929-022-00798-z)""

Notes

-------

We noticed there is a mismatch of reference list in Supplementary Table 2 of the publication, therefore we made an update for that. New supplementary Tables can be found [here](https://github.com/SysBioChalmers/DLKcat/tree/master/DeeplearningApproach/Results/figures)

Contact

-------

-   Feiran Li ([@feiranl](https://github.com/feiranl)), Chalmers

    University of Technology, Gothenburg, Sweden

-   Le Yuan ([@le-yuan](https://github.com/le-yuan)), Chalmers

    University of Technology, Gothenburg, Sweden

Last update: 2022-04-09