DLKcat: Deep Learning and Bayesian Approaches for kcat Prediction and ecGEM Construction

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

Last update: 2022-04-09

To use DLKcat:

Clone or download the DLKcat software package.
If you want to use the deep learning approach for kcat prediction, go to the 'DeeplearningApproach' folder and follow the instructions in the README file.
If you want to use the Bayesian approach to construct ecModels, go to the 'BayesianApproach' folder and follow the instructions in the README file.
Use the appropriate example files or your own data for testing and prediction, depending on your needs.
If you encounter any problems, check the instructions in the README file or contact the software authors.

We hope this helps you get started with the DLKcat software!