https://github.com/dohlee/rasp-pytorch
Reimplementation of RaSP, a deep neural network for rapid protein stability prediction, in PyTorch.
https://github.com/dohlee/rasp-pytorch
artificial-intelligence bioinformatics protein protein-stability self-supervised-learning
Last synced: about 1 month ago
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Reimplementation of RaSP, a deep neural network for rapid protein stability prediction, in PyTorch.
- Host: GitHub
- URL: https://github.com/dohlee/rasp-pytorch
- Owner: dohlee
- Created: 2023-03-30T12:18:34.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-04-05T13:02:06.000Z (about 2 years ago)
- Last Synced: 2025-04-16T20:57:05.462Z (about 2 months ago)
- Topics: artificial-intelligence, bioinformatics, protein, protein-stability, self-supervised-learning
- Language: Python
- Homepage:
- Size: 68.4 KB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# rasp-pytorch
Reimplementation of RaSP, a deep neural network for rapid protein stability prediction, in PyTorch.
You can find the official implementation of RaSP [here](https://github.com/KULL-Centre/papers/tree/main/2022/ML-ddG-Blaabjerg-et-al).
## Installation
```bash
$ pip install rasp-pytorch
```
## Training data
Training 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](https://github.com/KULL-Centre/papers/tree/main/2022/ML-ddG-Blaabjerg-et-al/data/train/cavity).
## Custom data preprocessing
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](https://github.com/dohlee/protein-structure-preprocessing).
## Citation
```bibtex
@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}
}
```