mutation-predictability

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

• System requirements
• Project structure
• Install instructions
• Reproducibility

System requirements

Hardware requirements

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.

Software requirements

OS requirements

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

Python requirements

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

Project structure

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

Install instructions

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.

Reproducibility

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.