Description

This repository contains the code and datasets to reproduce the results and figures and to train the models from our paper "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning".

For people interested in using the trained prediction model, we implemented a web server that allows an easy use of our trained model. The prediction tool can be run in a web-browser and does not require the installation of any software. Prediction results are usually ready within a few minutes.

For people interested in using a python function to achieve predictions of the trained model, we created a GitHub repository that allows an easy use of our trained model.

Downloading data folder

Before you can run the jupyter notebooks, you need to download and unzip a data folder from Zenodo. Afterwards, this repository should have the following strcuture:

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

Requirements for running the code in this GitHub repository

• python 3.7.7
• jupyter
• pandas 1.3.0
• torch 1.6.0
• numpy 1.21.2
• rdkit 2020.03.3
• fair-esm 0.3.1
• py-xgboost 1.2.0
• matplotlib 3.4.1
• hyperopt 0.25
• sklearn 0.22.1
• pickle
• Bio 1.78
• re 2.2.1
• drfp 0.3.6
• zeep 4.2.1

The listed packaged can be installed using conda and pip:

pip install torch
pip install numpy
pip install tensorflow
pip install fair-esm
pip install jupyter
pip install matplotlib
pip install hyperopt
pip install pickle
pip install biopython
pip instal drfp
pip install zeep
conda install pandas=1.3.0
conda install -c conda-forge py-xgboost=1.2.0
conda install -c rdkit rdkit