Protein-Nucleic Acid Potentials

A computational framework for deriving and calculating statistical potentials to analyze the interactions between proteins and nucleic acids (DNA/RNA).

Overview

This repository contains source code and datasets for generating knowledge-based potentials (or statistical potentials) specific to protein-nucleic acid complexes. These potentials are derived from known structures in the Protein Data Bank (PDB) and can be used for:

• Scoring protein-DNA/RNA docking poses.
• Evaluating the stability of protein-nucleic acid interfaces.
• Predicting binding affinities.

Theoretical Background

The potentials in this project are derived using the inverse Boltzmann law, converting the observed frequency of contacts between amino acids and nucleotides into energy scores.

Prerequisites

To run the scripts in this repository, you will need the following installed:

• Language: Python 3.8+ (or C++ if applicable)
• Libraries:
  • numpy
  • scipy
  • biopython (for PDB parsing)
  • matplotlib (for plotting potentials)

Installation

1. Clone the repository:

   git clone https://github.com/Arkoparno/Protein-nucleic-acid-potentials.git
   cd Protein-nucleic-acid-potentials

2. Install dependencies:

   pip install -r requirements.txt

   (If you do not have a requirements file, install manually using pip install numpy biopython)

Usage

1. Deriving Potentials from PDB

To generate new potential files based on a dataset of PDB structures:

python [train_potentials.py] --input_dir /path/to/pdb_files/ --output potentials.json

2. Scoring a Complex

To score a specific Protein-DNA complex using the derived potentials:

python [score_complex.py] --pdb 1A2B.pdb --potentials potentials.json

Output:

Interaction Energy: -45.23 kcal/mol
Interface Residues: ARG-12, LYS-15, DT-4...

Directory Structure

Protein-nucleic-acid-potentials/
├── data/                 # Sample PDB files or raw datasets
├── src/                  # Source code for calculations
│   ├── parser.py         # PDB parsing logic
│   ├── potentials.py     # Math and statistical logic
│   └── scorer.py         # Scoring functions
├── results/              # Output graphs and potential matrices
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation