Jue Wang (juewang@post.harvard.edu)
Doug Tischer (dtischer@uw.edu)
Sidney Lisanza (lisanza@uw.edu)
David Juergens (davidcj@uw.edu)
Joe Watson (jwatson3@uw.edu)
This repository contains code for protein hallucination or inpainting, as
described in our
preprint. Code
for postprocessing and analysis scripts included in scripts/.
Every use case of the hallucination and inpainting code in this repository (and beyond) can be performed by RFDiffusion, more quickly and with higher-quality results. Please see the RFDiffusion Github repository and use that instead of this. We will be discontinuing support for this code (unless you are trying to use it for something very specific and strange, in which case contact us directly by email).
All code and neural network weights are open source under the BSD license. See LICENSE.
- Clone the repository:
git clone https://github.com/RosettaCommons/RFDesign.git
cd RFDesign
- Create environment and install dependencies:
cd envs
conda env create -f SE3-nvidia.yml
- Download weights (this step can be skipped if you downloaded the Zenodo version of this repo):
cd hallucination/weights/rf_Nov05
wget http://files.ipd.uw.edu/pub/rfdesign/weights/BFF_last.pt
cd inpainting/weights/
wget http://files.ipd.uw.edu/pub/rfdesign/weights/BFF_mix_epoch25.pt
- Run tests
cd hallucination/tests/
./run_tests.sh
cd inpainting/tests/
./run_tests.sh
UPDATE 2022-9-27: Tim O'Donnell generously provided a Dockerfile to make installation easier. You can try doing the following to install.
A Docker image for running RFDesign on a GPU can be built and run as follows:
cd docker
docker build . -t rfdesign/rfdesign:latest
nvidia-docker run -it rfdesign/rfdesign:latest /root/miniconda3/envs/rfdesign-cuda/bin/python RFDesign/hallucination/hallucinate.py --help
The resulting image will be able to run inpainting, hallucination, and the af2_metrics.py script. Functionality that relies on pyrosetta is not supported.
If you want/need to configure your environment manually, here are the packages in our environment:
- python 3.8
- pytorch 1.10.1
- cudatoolkit 11.3.1
- numpy
- scipy
- requests
- packaging
- pytorch-geometric (installation instructions)
- dgl (installation instructions)
- lie_learn
- icecream (for
inpainting.py)
See READMEs in hallucination/ and inpainting/ subfolders.
J. Wang, S. Lisanza, D. Juergens, D. Tischer, et al. Deep learning methods for designing proteins scaffolding functional sites. bioRxiv (2021). link
M. Baek, et al., Accurate prediction of protein structures and interactions using a three-track neural network, Science (2021). link
An earlier version of our hallucination method can be found at the trdesign-motif repo and published at:
D. Tischer, S. Lisanza, J. Wang, R. Dong, I. Anishchenko, L. F. Milles, S. Ovchinnikov, D. Baker. Design of proteins presenting discontinuous functional sites using deep learning. (2020) bioRxiv link
Our work is based on previous hallucination methods for unconstrained protein generation and fixed-backbone sequence design (trDesign repo):
I Anishchenko, SJ Pellock, TM Chidyausiku, ..., S Ovchinnikov, D Baker. De novo protein design by deep network hallucination. (2021) Nature link
C Norn, B Wicky, D Juergens, S Liu, D Kim, B Koepnick, I Anishchenko, Foldit Players, D Baker, S Ovchinnikov. Protein sequence design by conformational landscape optimization. (2021) PNAS link
This repository includes copies of: