Reimplementation of RaSP, a deep neural network for rapid protein stability prediction, in PyTorch. You can find the official implementation of RaSP here.
$ pip install rasp-pytorchTraining data for cavity model (2,315 high-resolution protein structures obtained using the PISCES server with maximal sequence identity set to 30%) was downloaded from official GitHub repository.
Although the reproduction was done using the official training data, we can also compose custom training dataset using protein-structure-preprocessing pipeline, which can be found here.
@article{Blaabjerg2022.07.14.500157,
author = {Blaabjerg, Lasse M and Kassem, Maher M and Good, Lydia L and Jonsson, Nicolas and Cagiada, Matteo and Johansson, Kristoffer E and Boomsma, Wouter and Stein, Amelie and Lindorff-Larsen, Kresten},
doi = {10.1101/2022.07.14.500157},
journal = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
title = {{Rapid protein stability prediction using deep learning representations}},
url = {https://www.biorxiv.org/content/early/2022/07/15/2022.07.14.500157},
year = {2022}
}