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: 7 months ago
JSON representation
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 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-04T15:32:14.000Z (over 2 years ago)
- Last Synced: 2025-04-05T09:04:07.912Z (10 months ago)
- Topics: bayesian, deep-learning, enzyme-constraints, enzyme-turnover-number, kcat, kinetics
- Language: Python
- Homepage:
- Size: 46.2 MB
- Stars: 151
- Watchers: 4
- Forks: 57
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
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