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.
- input for the prediction is the Protein sequence and Substrate SMILES structure/Substrate name, please check the file in DeeplearningApproach/Code/example/input.tsv
- output is the correponding kcat value

Citation

Please cite the paper Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction""

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