Skip to content

PyTorch implementation for our paper "Proximal Exploration for Model-guided Protein Sequence Design"

License

Notifications You must be signed in to change notification settings

HeliXonProtein/proximal-exploration

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Proximal Exploration (PEX)

This repository contains a PyTorch implementation of our paper Proximal Exploration for Model-guided Protein Sequence Design published at ICML 2022. Proximal Exploration (PEX) is a variant of directed evolution, which prioritizes the search for low-order mutants. Based this local-search mechanism, a model architecture called Mutation Factorization Network (MuFacNet) is developed to specialize in the local fitness landscape around the wild type.

Installation

The dependencies can be set up using the following commands:

conda create -n pex python=3.8 -y
conda activate pex
conda install pytorch=1.10.2 cudatoolkit=11.3 -c pytorch -y
conda install numpy=1.19 pandas=1.3 -y
conda install -c conda-forge tape_proteins=0.5 -y
pip install sequence-models==1.2.0

Clone this repository and download the oracle landscape models by the following commands:

git clone https://github.com/HeliXonProtein/proximal-exploration.git
cd proximal-exploration
bash download_landscape.sh

Usage

Run the following commands to reproduce our main results shown in section 5.1. There are eight fitness landscapes to support a diverse evaluation on black-box protein sequence design.

python run.py --alg=pex --net=mufacnet --task=avGFP  # Green Fluorescent Proteins
python run.py --alg=pex --net=mufacnet --task=AAV    # Adeno-associated Viruses
python run.py --alg=pex --net=mufacnet --task=TEM    # TEM-1 β-Lactamase
python run.py --alg=pex --net=mufacnet --task=E4B    # Ubiquitination Factor Ube4b
python run.py --alg=pex --net=mufacnet --task=AMIE   # Aliphatic Amide Hydrolase
python run.py --alg=pex --net=mufacnet --task=LGK    # Levoglucosan Kinase
python run.py --alg=pex --net=mufacnet --task=Pab1   # Poly(A)-binding Protein
python run.py --alg=pex --net=mufacnet --task=UBE2I  # SUMO E2 conjugase

In the default configuration, the protein fitness landscape is simulated by a TAPE-based oracle model. By adding the argument --oracle_model=esm1b, the landscape simulator is switched to an oracle model based on ESM-1b.

Contact

Please contact zhizhour[at]helixon.com for any questions related to the source code.

About

PyTorch implementation for our paper "Proximal Exploration for Model-guided Protein Sequence Design"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published