Peptide Design Genetic Algorithm (PDGA)

PDGA is a modular genetic algorithm framework designed to generate peptide sequences with desired building blocks and properties. It integrates modular operators for fitness evaluation, selection, crossover, and mutation. In addition, PDGA uses a flexible configuration system, a building block manager for processing chemical building blocks, and a dedicated results handler to collect and export experimental hits.

Features

• Modular Design: Easily swap out building blocks, fitness, selection, crossover, and mutation operators.
• Simple Implementation: PDGA is a simple implementation that can be easily adapted to different use cases for peptide design.
• Building Block Management: BuildingBlockManager handles data loading and sequence translation of building blocks
• Results Handling: ResultsHandler collects hit sequences (those meeting a fitness cutoff) and exports them to a CSV file with configurable sorting.
• Configurable via CLI: Experiment parameters can be provided via a configuration file as well as command-line arguments, enabling easy switching between experiments.

Installation

1. Clone the Repository:

   git clone https://github.com/markusorsi/pdga.git
   cd pdga

2. Create a Virtual Environment and Install Dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Prepare Your Data:

   Replace of modify the CSV files containing building blocks in the bblocks/ directory:

   • bb_monomers.csv
   • bb_ncaps.csv
   • bb_branches.csv
   • bb_additional.csv

Usage

Running PDGA

You can run PDGA from the command line. The main script accepts various command-line arguments to override configuration defaults.

For example:

python main.py --config config.py --pop_size 100 --n_iterations 500 --run_id experiment_01

The --config parameter allows you to specify a configuration file containing default settings. All parameters (such as query, population size, mutation ratio, fitness function, selection strategy, crossover method, etc.) can be overridden via the terminal.

Command-Line Arguments

• --config: Path to the configuration file (default: config.py).
• --query: Target query used for fitness evaluation.
• --query_format: Format of the query (e.g., smiles).
• --pop_size: Population size.
• --pop_selection: Number of individuals selected as parents.
• --mutation_ratio: Ratio of individuals to be mutated.
• --cutoff: Fitness cutoff to record hits.
• --fitness_function: Identifier for the fitness function to use (e.g., map4c, atompair, mxfp).
• --selection_strategy: Identifier for the selection strategy (e.g., maximize).
• --crossover_method: Identifier for the crossover method (e.g., single_point, skip).
• --n_iterations: Number of generations to run.
• --run_id: Identifier for the run (used in output filenames).
• --maximize: The goal of the optimization study.
• --seed: Random seed for reproducibility.

File Structure

├── bblocks
│   ├── bb_additional.csv     # Additional building blocks.
│   ├── bb_branches.csv       # List of branch building blocks.
│   ├── bbmanager.py          # BuildingBlockManager implementation.
│   ├── bb_monomers.csv       # List of monomers concatenable to form peptides.
│   └── bb_ncaps.csv          # List of N-terminal caps.
├── config.py                 # Default configuration file.
├── logs                      # Log directory for experiments.
├── main.py                   # Entry point script.
├── operators
│   ├── crossover             # Crossover operators.
│   ├── fitness               # Fitness functions.
│   ├── mutation              # Mutation operators.
│   └── selection             # Selection operators.
├── pdga.py                   # Main PDGA implementation.
├── README.md                 # This file.  
├── requirements.txt          # Python dependencies.
├── results                   # Output directory for experiments.
└── utils.py                  # ResultsHandler implementation.

Configuration File Example

Below is a sample config.py file:

CONFIG = {
    # Query settings
    'query': 'SEQVENCE',
    'query_format': 'sequence',

    # Population settings
    'pop_size': 50,
    'pop_selection': 10,

    # Genetic operators parameters
    'mutation_ratio': 0.5,
    'cutoff': 300,
    'fitness_function': 'mxfp',
    'selection_strategy': 'minimize',
    'crossover_method': 'single_point',

    # Algorithm runtime settings
    'n_iterations': 1000,
    'seed': 42,

    # Output settings
    'run_id': 'test_run',
    'maximize': False,
}

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or feedback, please contact [email protected].

Citation

@article{orsi2025navigating,
  title = {Navigating a 1E+60 Chemical Space of Peptide/Peptoid Oligomers},
  author = {Orsi, Markus and Reymond, Jean-Louis},
  journal = {Molecular Informatics},
  volume = {44},
  number = {1},
  pages = {e202400186},
  year = {2025},
  publisher = {Wiley Online Library}
}