Skip to content

Latest commit

 

History

History
76 lines (55 loc) · 4.43 KB

README.md

File metadata and controls

76 lines (55 loc) · 4.43 KB
Raw

QA-PBP-RL-Nexus

This repository contains code and data to generate and validate plastic binding peptides (PBPs) for the manuscript cited below.

Overview

RL_PPO_Peptides/ contains code to run PPO to discover plastic-binding peptides for a given system conformation and associated Potts model.

QA_peptide_generator/ contains code to run QA to discover plastic-binding peptides for a system conformation and associated Potts model.

demo/ contains code and example inputs for peptide discovery with QA and PPO.

Data Folder/ contains the following data used in the manuscript:

  • Data Folder/PottsModel_Energies contains one-body energies (SingleEnergy.txt) and two-body energies (Pairwise.txt) for each system conformations (termed a "bb" for backbone) for polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET).

  • Data Folder/PPO_All_Sequences_All_Conformations contains PPO solutions for all system conformations. As described in the manuscript, PPO was only performed for a subset of the system conformations for polyethylene. The system conformation numbering is consistent with the directory PottsModel_Energies/PE (e.g., bb1 is the same system in these two directories).

  • Data Folder/Peptide Design with QA+RL PNAS Nexus SI Data File.xlsx contains all raw data for the manuscript. The overview sheet summarizes the contents.

System Requirements

Operating System

This repository has been developed and tested on macOS, ensuring full compatibility with this operating system. However, the provided scripts are designed to be platform-independent and should work seamlessly on other versions of macOS and Windows.

Hardware Requirements

The PPO models do not require any non-standard hardware and can be run on a typical computer. The code also provides the option to utilize a GPU via the PyTorch library for speedup in the reinforcement learning process. Quantum Annealing is performed on the D-Wave quantum computer. An API token from D-Wave is required to successfully run the QA part of the code.

The code has been tested on a system with the following specifications:

  • Apple M1 Pro
  • 16GB of RAM
  • macOS Sonoma

Package Requirements

  • python 3.9.7
  • pandas 2.0.0
  • pytorch 2.0.0
  • numpy 1.24.2
  • dimod 0.10.15
  • dwave-hybrid 0.6.10
  • tqdm 4.64.1
  • openpyxl 3.1.2

Installation Guide

The source code is not distributed as a package, therefore, no installation is required.

Clone the repository: Clone the repository to a local directory using the following command:

https

git clone https://github.com/PEESEgroup/QA-PBP-RL-Nexus.git

Demo

Detailed examples of how to use our model to generate PBPs using Proximal Policy Optimization (PPO) are provided in demo/PPO_pbp.py; This Python file contains the base code and basic hyperparameters required to train the policy for peptide generation. Additionally, demo/ppo_pbp_generation.py contains the code to generate peptides using the learned policy. GPU support using CUDA is provided to enhance performance, and we have attempted compatibility with PyTorch for efficient GPU utilization.

Detailed examples of how to use our model to generate PBPs using Quantum Annealing are provided in demo/qa_pbp_generation.py. This Python file contains the base code required to generate new peptides. Access to D-Wave hardware via an API token is necessary to utilize the Quantum Annealer. For demo purposes, we have used a basic sampler to replicate the workflow.

Instructions for Use

To reproduce the results presented in our paper, please follow these steps:

  1. Navigate to the demo/ directory and examine the readme section and provided .py files with sample inputs.
  2. Ensure that your environment meets the package and system specifications outlined in the instructions provided within the repository.

Note: Due to the stochastic nature of the computational models, you may not generate identical peptides in multiple runs. However, you can expect to observe the same distributions of peptide properties (such as total energy and amino acid distribution) as reported in the paper, provided the same design parameters are used.

Citation

@article{,
author = {Jeet Dhoriyani, Michael T. Bergman, Carol K. Hall, and Fengqi You},
title = {Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution},
journal = {},
volume = {},
number = {},
pages = {},
doi = {},
abstract = {}}