/trRosetta2

Repository for publicly available deep learning models developed in Rosetta community

Primary LanguagePythonMIT LicenseMIT

trRosetta2

This package contains deep learning models and related scripts used by Baker group in CASP14.

Installation

Linux/Mac

  1. clone the package
git clone https://github.com/RosettaCommons/trRosetta2
cd trRosetta2
  1. create conda environment using one of the .yml files: casp14-baker-linux-cpu.yml, casp14-baker-linux-gpu.yml, casp14-baker-mac-cpu.yml
conda env create -f casp14-baker-linux-gpu.yml
conda activate casp14-baker
  1. download network weights [1.1G]
wget https://files.ipd.uw.edu/pub/trRosetta2/weights.tar.bz2
tar xf weights.tar.bz2
  1. download and install third-party software
./install_dependencies.sh
  1. download sequence and structure databases
# uniclust30 [46G]
wget http://wwwuser.gwdg.de/~compbiol/uniclust/2020_06/UniRef30_2020_06_hhsuite.tar.gz
mkdir -p UniRef30_2020_06
tar xf UniRef30_2020_06_hhsuite.tar.gz -C ./UniRef30_2020_06

# 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 the newly created casp14-baker conda environment (link).

Usage

mkdir -p examples/T1078
./run_pipeline.sh example/T1078.fa example/T1078

Links

References

[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