Protein Docking Analysis Script

Overview

This script automates the analysis of protein-ligand docking results, focusing on:

• Identifying the best docking poses based on energy scores and residue-ligand interaction frequencies.
• Preparing results for downstream visualization and analysis.
• Normalizing interaction data for comparative studies.

It is designed for computational biology and drug discovery pipelines, offering a robust framework for studying protein-ligand interactions.

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Features

• Best Docking Pose Selection: Identifies the docking pose with the best energy and interaction scores.
• Residue-Ligand Interaction Analysis: Calculates contact frequencies between receptor residues and ligand atoms using distance thresholds.
• Result Normalization: Outputs normalized scores for better comparison of docking results.
• Data Preparation for Visualization: Converts docking result files into standardized formats compatible with visualization tools like JSmol.

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Requirements

• Python 3.8 or higher
• Python libraries:
  • os
  • shutil
  • json
  • math
  • datetime

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Installation

• Clone the Repository:

  git clone https://github.com/yourusername/protein-docking-analysis.git
  cd protein-docking-analysis
  

Usage

• Best Docking Pose Selection Identify the best docking result and prepare it for further analysis:

from docking_analysis import best_result

## Example usage
best_result(
    file_name="file_name.pdb",
    monomer="monomer",
    rec_lig="receptor_ligand",
    receptor="receptor",
    ligand="ligand")

• Residue-Ligand Interaction Analysis Calculate contact frequencies and generate a residue-ligand interaction dictionary:

from docking_analysis import result_dict_generator

## Example usage
result_dict = result_dict_generator(
    threshold=5.0, 
    monomer="monomer", 
    rec_lig="receptor_ligand", 
    receptor="receptor", 
    ligand="ligand")

• Visualization-Ready Data Preparation Normalize interaction scores and prepare files for visualization:

from docking_analysis import normalize_results

## Example usage
normalize_results(
    monomer_json="monomer_results.json",
    receptor="receptor",
    ligand="ligand",
    final_json="final_normalized_results.json")