Jue Wang ([email protected])
Doug Tischer ([email protected])
Sidney Lisanza ([email protected])
David Juergens ([email protected])
Joe Watson ([email protected])
This repository contains code for protein hallucination or inpainting, as
described in our
preprint. Code
for postprocessing and analysis scripts included in scripts/.
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: