Description

This repository contains an easy-to-use Python function for the kcat prediction model from our paper "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning".

Predicting kcat values for enzyme-reaction pairs

The kcat prediction model was only trained with natural enzyme-reaction pairs with wild-type enzymes. Hence, the model will not be good at predicting kcat for mutants or for non-natural reactions of enzymes.

Downloading data folder

Before you can run the kcat prediction function, you need to download and unzip a data folder. Afterwards, this repository should have the following strcuture:

├── code                   
├── data                    
└── README.md

substrate and product representations

You can use InChI strings, KEGG Compound IDs, and SMILES strings as substrate/product representations.

Requirements

• python 3.7
• jupyter
• pandas 1.1.3
• torch 1.11.0
• numpy
• rdkit 2020.09.1
• fair-esm 0.4.0
• py-xgboost 1.6.1

The listed packages can be installed using conda and anaconda:

pip install pandas==1.1.3
pip install torch==1.11.0
pip install numpy
pip install fair-esm==0.4.0
conda install -c conda-forge py-xgboost=1.6.1
conda install -c rdkit rdkit=2020.09.1

Content

There is a Jupyter notebook "Tutorial kcat prediction.ipynb" in the folder "code" that contains an example on how to use the kcat prediction function.

Problems/Questions

If you face any issues or problems, please open an issue.