/DLKcat

Deep learning and Bayesian approach applied to enzyme turnover number for the improvement of enzyme-constrained genome-scale metabolic models (ecGEMs) reconstruction

Primary LanguagePython

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 under the DeeplearningApproach.

Citation

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

Contact

  • Feiran Li (@feiranl), Chalmers University of Technology, Gothenburg, Sweden
  • Le Yuan (@le-yuan), Chalmers University of Technology, Gothenburg, Sweden

Last update: 2022-04-09