https://github.com/dohlee/foldingdiff-pytorch
An unofficial re-implementation of FoldingDiff, a diffusion-based generative model for protein backbone structure generation.
https://github.com/dohlee/foldingdiff-pytorch
computational-biology diffusion diffusion-models protein protein-backbones protein-structure
Last synced: 4 months ago
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An unofficial re-implementation of FoldingDiff, a diffusion-based generative model for protein backbone structure generation.
- Host: GitHub
- URL: https://github.com/dohlee/foldingdiff-pytorch
- Owner: dohlee
- Created: 2023-05-10T09:03:24.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-07-26T08:36:09.000Z (about 2 years ago)
- Last Synced: 2025-05-21T14:24:37.807Z (5 months ago)
- Topics: computational-biology, diffusion, diffusion-models, protein, protein-backbones, protein-structure
- Language: Jupyter Notebook
- Homepage:
- Size: 28.8 MB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# foldingdiff-pytorch
[](https://github.com/Lightning-AI/lightning)
An unofficial re-implementation of FoldingDiff, a diffusion-based generative model for protein backbone structure generation.
The official implementation of FoldingDiff can be found [here](https://github.com/microsoft/foldingdiff).
## Gallery
### Noising
### Denoising
## Installation
Install through pip.
```bash
$ pip install foldingdiff-pytorch
```
## Quickstart
### Training
```bash
$ python -m foldingdiff_pytorch.train --meta data/meta.csv \
--data-dir data/npy --batch-size 64
```
### Sampling
```bash
$ python -m foldingdiff_pytorch.sample --ckpt [CHECKPOINT_PATH] \
--timepoints 1000 --out [OUTPUT_PATH]
```
### Sampling pipeline
With the `snakemake` command below, you can simply run unconditional protein backbone generation pipeline to obtain `.pt` files containing backbone coordinates and `.gif` files showing the whole denoising process.
```bash
$ snakemake -s sample.smk -j1
```
## Downloading and preprocessing training data
Download non-redundant protein backbone structure data (40% similary cutoff) from CATH.
```bash
$ wget ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/latest-release/non-redundant-data-sets/cath-dataset-nonredundant-S40.pdb.tgz
```
Extract the downloaded file and attach `.pdb` extension to files
```bash
$ tar xvf cath-dataset-nonredundant-S40.pdb.tgz && cd dompdb
$ for f in *; do mv "$f" "$f.pdb"; done
```
Run `snakemake` pipeline to convert pdb files to `npy` files containing angle information of shape (n, 6).
```
$ snakemake -s preprocess.smk -prq -j [CORES] --keep-going
```
## Reproduction status
Model training for reproduction is currently running. The live training log is available at [here](https://api.wandb.ai/links/dohlee/1drc1ujg).
### Ramachandran plot
Visualized Ramachandran plot for 10 samples of length 64 for sanity check while training.
Looks like the model is learning to produce reasonable secondary structures.
## Citation
```bibtex
@misc{wu2022protein,
title={Protein structure generation via folding diffusion},
author={Kevin E. Wu and Kevin K. Yang and Rianne van den Berg and James Y. Zou and Alex X. Lu and Ava P. Amini},
year={2022},
eprint={2209.15611},
archivePrefix={arXiv},
primaryClass={q-bio.BM}
}
```