The package summarizes developments on the use of trRosetta structure prediction network for various protein design applications. We provide core codes for the following papers:
01. I Anishchenko, TM Chidyausiku, S Ovchinnikov, SJ Pellock, D Baker. De novo protein design by deep network hallucination. (2020) bioRxiv, doi:10.1101/2020.07.22.211482. PDF
02. C Norn, B Wicky, D Juergens, S Liu, D Kim, B Koepnick, I Anishchenko, Foldit Players, D Baker, S Ovchinnikov. Protein sequence design by explicit energy landscape optimization. (2020) bioRxiv, doi:10.1101/2020.07.23.218917. LINK
tensorflow (tested on versions 1.13 and 1.14)
# download package
git clone --recursive https://github.com/gjoni/trDesign
cd trDesign
# download trRosetta network weights
wget https://files.ipd.uw.edu/pub/trRosetta/model2019_07.tar.bz2
tar xf model2019_07.tar.bz2 -C trRosetta/
# download background network weights
wget https://files.ipd.uw.edu/pub/trRosetta/bkgr2019_05.tar.bz2
mkdir -p background && tar xf bkgr2019_05.tar.bz2 -C background/
