This repository contains the data and code used to generate results and corresponding figures of the paper "Enzyme Structure Correlates With Variant Effect Predictability".

• System requirements
• Project structure
• Install instructions
• Reproducibility

predictability can be run on a standard computer without extensive hardware configurations. GPU availability is not necessary, but will greatly speed up training runs of the RITA regressor.

The predictability package is supported for macOS and Linux and tested on macOS Sonoma 14.5.

predictability requires python ≥ 3.8. All requirements and the corresponding versions are listed in the requirements.txt file.

All experiments and processing of results are organized in notebooks, which can be run by installing the predictability package.

Clone the repository and install with

git clone https://github.com/florisvdf/mutation-predictability.git
cd mutation-predictability
pip install .

The Potts Regressor model of the predictability package makes use of gremlin_cpp. To use the Potts Regressor, make sure that gremlin_cpp is installed and is added to $PATH.

Installation on a typical computer should take no longer than 10 minutes.

Results can be reproduced by simply executing all notebooks under the notebooks directory. Plots can be generated by executing the notebooks/plotting.ipynb notebook. Different sample assignment to train and test folds can be achieved by executing the notebooks while changing the variable seed in the second cell.

Copyright 2024 International Flavors and Fragrances, Wageningen University & Research

All software is licensed under the MIT License; you may not use this file except in compliance with the MIT license. You may obtain a copy of the MIT license at: https://mit-license.org

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Unless required by applicable law or agreed to in writing, all software and materials distributed here under the MIT or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.