GitHub repo for RoseTTAFold2 (Kuhlman lab version). WARNING: templates have not been set up yet.
- Clone the package
git clone https://github.com/Kuhlman-Lab/RosettaFold2.git
cd RoseTTAFold2
- Create conda environment. Here, try RF2-linux.yml and RosettaFold2-linus.yml and use the one that works :).
# create conda environment for RoseTTAFold2.
conda env create -f RF2-linux.yml
You also need to install NVIDIA's SE(3)-Transformer (please use SE3Transformer in this repo to install).
conda activate RF2
cd SE3Transformer
pip install --no-cache-dir -r requirements.txt
python setup.py install
- Download pre-trained weights under network directory
cd network
wget https://files.ipd.uw.edu/dimaio/RF2_apr23.tgz
tar xvfz RF2_apr23.tgz
cd ..
Prepare to run
conda activate RF2
cd example
See examples_longleaf folder for Amrita's examples of how to use! Below are the instructions that came with the UW version:
../run_RF2.sh rcsb_pdb_7UGF.fasta -o 7UGF
../run_RF2.sh rcsb_pdb_8HBN.fasta --pair -o 8HBN
../run_RF2.sh rcsb_pdb_7ZLR.fasta --pair -o 7ZLR
../run_RF2.sh rcsb_pdb_7YTB.fasta --symm C6 -o 7YTB
../run_RF2.sh rcsb_pdb_7LAW.fasta --symm C3 --pair -o 7LAW
Predictions will be output to the folder 1XXX/models/model_final.pdb. B-factors show the predicted LDDT. A json file and .npz file give additional accuracy information.
The script run_RF2.sh
has a few extra options that may be useful for runs:
Usage: run_RF2.sh [-o|--outdir name] [-s|--symm symmgroup] [-p|--pair] [-h|--hhpred] input1.fasta ... inputN.fasta
Options:
-o|--outdir name: Write to this output directory
-s|--symm symmgroup (BETA): run with the specified spacegroup.
Understands Cn, Dn, T, I, O (with n an integer).
-p|--pair: If more than one chain is provided, pair MSAs based on taxonomy ID.
-h|--hhpred: Run hhpred to generate templates