dl_binder_design

This repo contains the scripts described in the paper Improving de novo Protein Binder Design with Deep Learning.

Setup:

Conda Environment

• Ensure that you have the Anaconda or Miniconda package manager
• Ensure that you have the PyRosetta channel included in your ~/.condarc
• Your ~/.condarc should look something like this:

channels: 
- https://USERNAME:PASSWORD@conda.graylab.jhu.edu
# some of PyRosetta versions might require conda-forge channel, uncomment line below if you want to use it
# - conda-forge
- defaults

• More information about conda installing PyRosetta may be found here: https://www.pyrosetta.org/downloads
• Clone this repo
• Navigate to <base_dir>/include
• Run conda env create -f dl_binder_design.yml

Silent Tools

The scripts contained in this repository work with a type of file called silent files. These are essentially a bunch of compressed .pdb files that are all stored in one file. Working with silent files is conventient and saves a lot of disk space when dealing with many thousands of structures.

Brian Coventry wrote a bunch of really nice commandline tools (called silent_tools) to manipulate silent files. These tools are included in this repository but may also be downloaded separately from this GitHub repo.

The two commands that allow you to go from pdb to silent file and back are the following:

pdbs to silent: <base_dir>/silentfrompdbs *.pdb > my_designs.silent

silent to pdbs: <base_dir>/silentextract all_structs.silent

NOTE: Some silent tools require PyRosetta and will fail if run in a Python environment without access to PyRosetta.

AlphaFold2 Complex Prediction

Running the interface prediction script is simple:

<base_dir>/af2_initial_guess/interfaceAF2predict.py -silent my_designs.silent

This will create a file titled out.silent containing the AF2 predictions of your designs. It will also output a file titled out.sc with the scores of the designs, pae_interaction is the score that showed the most predictivity in the experiments performed in the paper.

NOTE: This script expects your binder design to be the first chain it receives. The binder will be predicted from single sequence and with an intial guess. The target chains will be fixed to the input structure. The script also expects your residue indices to be unique, ie. your binder and target cannot both start with residue 1.

ProteinMPNN-FastRelax Binder Design

Running design with ProteinMPNN-FastRelax cycling is also simple:

<base_dir>/mpnn_fr/dl_interface_design.py -silent my_designs.silent

This will create a file titled out.silent containing your designs. This file can be fed directly to AF2 interface prediction.

NOTE: This script expects your binder design to be the first chain it receives. This script is robust to non-unique indices, unlike the AF2 interface script.