This package contains deep learning models and related scripts used by Baker group in CASP14.
# 1) clone package
git clone https://github.com/RosettaCommons/trRosetta2
cd trRosetta2
# 2) create conda environment
conda env create -f casp14-baker.yml
conda activate casp14-baker
# 3) download network weights [1.1G]
wget https://files.ipd.uw.edu/pub/trRosetta2/weights.tar.bz2
tar xf weights.tar.bz2
# 4) download and install third-party software
./install_dependencies.sh
# 5) download sequence and structure databases
# uniclust30 [46G]
wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gz
tar xf UniRef30_2020_06_hhsuite.tar.gz
# structure templates [8.3G]
wget https://files.ipd.uw.edu/pub/trRosetta2/pdb100_2020Mar11.tar.gz
tar xf pdb100_2020Mar11.tar.gz
Obtain a PyRosetta licence and install the package in casp14-baker conda environment (link).
mkdir -p examples/T1078
./run_pipeline example/T1078.fa example/T1078
[1] I Anishchenko, M Baek, H Park, J Dauparas, N Hiranuma, S Mansoor, I Humphrey, D Baker. Protein structure prediction guided by predicted inter-residue geometries. In: CASP14 Abstract Book, 2020
[2] H Park, M Baek, N Hiranuma, I Anishchenko, S Mansoor, J Dauparas, D Baker. Model refinement guided by an interplay between Deep-learning and Rosetta. In: CASP14 Abstract Book, 2020
[3] M Baek, I Anishchenko, H Park, I Humphrey, D Baker. Protein oligomer structure predictions guided by predicted inter-chain contacts. In: CASP14 Abstract Book, 2020