This repo contains the code for our paper " BayeStab : Predicting effects of mutations on protein sta-bility with uncertainty quantification"
by Shuyu Wang*, Hongzhou Tang
Here we pioneered a deep graph neural network based method for predicting protein stability change upon mutation.
- Python 3.7
- Pytorch
- RDKit
- Rosetta
- numpy
- CUDA
- sklearn
BayeStab ΔΔG prediction is accomplished through a multi-step protocol. Each step relies on a specific third-party software that needs to be installed first. In the following, we outline the steps to install them.
Clone BayeStab to a local directory.
git clone https://github.com/HongzhouTang/BayeStab.git
- Go to https://els2.comotion.uw.edu/product/rosetta to get an academic license for Rosetta.
- Download Rosetta 3.13 (source + binaries for Linux) from this site: https://www.rosettacommons.org/software/license-and-download
- Extract the tarball to a local directory from which Rosetta binaries can be called by specifying their full path.