Awesome
TrRosetta-based protein hallucination
2021-10-18
Doug Tischer (dtischer at uw.edu)
Jue Wang (jue at post.harvard.edu)
Sidney Lisanza (lisanza at uw.edu)
This repository contains scripts for performing protein design by gradient descent through the structure-prediction neural network TrRosetta. The method is similar to that of trDesign except our focus is generating scaffolds for functional motifs.
This code accompanies the paper:
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, doi:10.1101/2020.07.22.211482.
Requirements
tensorflow
(tested on1.14
)scwrl4
(optional, for sidechains. download)pyrosetta
(2020.10+release.46415fa
, for obtaining structural models. see pyrosetta.org)
Installation & Usage
Clone git repository:
git clone https://github.com/dtischer/trdesign-motif.git
Run examples:
cd example
./run_example.sh # generates scaffold for PD-1 interface motif versus PD-L1
./run_example2.sh # same task as above, but using a different trRosetta version (see below)
Fold design model from predicted pairwise distances & angles (first run the
example above, then run this in the example
subfolder):
sequence_design/fold.sh output/
Additional downstream analyses can be done using the scripts in scoring/
.
Contents
hallucination
: contains hallucination script design.py
. see code for full
list of command-line options.
hallucination_grid
: an alternative hallucination script using a more recent
version of trRosetta. This version is slightly more accurate at structure
prediction, and also predicts the probability that some binned 3D position is
occupied by some other residue, in the reference frame of each residue.
sequence_design
: scripts for folding design models from hallucinated pairwise
distances and angles (fold.sh
) and for using Rosetta Fastdesign to design
better sequences onto the hallucinated backbones.
scoring
: various scripts to compute metrics on the designs generated by the
hallucination script.