---
title: 'HBAT: An updated Python Reimplementation for Hydrogen Bond Analysis in 
  Macromolecular Structures'
tags:
  - Python
  - structural biology
  - hydrogen bonds
  - molecular interactions
  - protein structures
  - bioinformatics
authors:
  - name: Abhishek Tiwari
    orcid: 0000-0003-2222-2395
    affiliation: 1
affiliations:
 - name: Independent Researcher
   index: 1
date: 3 August 2025
bibliography: paper.bib
---

# Summary

The Hydrogen Bond Analysis Tool (HBAT) is a comprehensive Python package for automated analysis of non-covalent interactions in macromolecular structures. Originally developed in Perl/Tk and published in 2007 [@tiwari2007hbat], latest version of HBAT has been completely rewritten in Python with enhanced functionality. The software identifies and analyzes hydrogen bonds, halogen bonds, and X-H...$\pi$ interactions using geometric criteria, providing detailed statistical analysis and advanced visualization capabilities for cooperativity chains. Latest version of HBAT offers improved graphical interface (tkinter), a simple command-line interfaces, and a developer-friendly API interface making it accessible to users with different computational backgrounds and workflow requirements.

# Statement of need

Hydrogen bonds and other weak interactions are fundamental to protein structure, stability, and function. With over 200,000 structures in the Protein Data Bank [@berman2000protein], there is an increasing need for automated tools to analyze these interactions systematically. While several tools exist for hydrogen bond analysis [@mcdonald1994satisfying; @lindauer1996hbexplore], they often lack comprehensive coverage of interaction types, modern interfaces, or advanced visualization capabilities.

The original HBAT [@tiwari2007hbat] was developed in Perl/Tk with a Windows-only GUI, limiting its adoption in modern computational environments. The software needed modernization to address current research needs including: (1) cross-platform compatibility, (2) support for modern Python scientific computing ecosystems and extensibility, (3) enhanced visualization capabilities, (4) improved performance for large structures, and (5) flexible output formats for downstream analysis.

HBAT addresses these limitations by providing a modern, cross-platform Python implementation that integrates seamlessly with contemporary structural biology workflows. The software is particularly valuable for researchers in structural biology, computational chemistry, and drug design who need detailed analysis of molecular interactions.

![Latest update to HBAT usage tkinter to provide a cross-platform graphical user interface (GUI))](https://static.abhishek-tiwari.com/hbat/hbat-window-v1.png)

# Key Enhancements

Latest HBAT update introduces several major improvements over the original 2007 version:

**Reimplementation in Python**: Complete rewrite from Perl/Tk to Python, enabling integration with the scientific Python ecosystem including NumPy [@harris2020numpy], Matplotlib [@hunter2007matplotlib], and NetworkX [@hagberg2008networkx].

**Cross-Platform Compatibility**: Unlike the original Windows-only version, HBAT now runs on Windows, macOS, and Linux systems.

**Enhanced Interaction Analysis**: Expanded coverage includes hydrogen bonds (O-H...O, N-H...O, N-H...N, C-H...O), halogen bonds (C-X...Y where X=F,Cl,Br,I), and X-H...$\pi$ interactions with aromatic systems.

**Cooperativity chain Visualization**: Two rendering engines for cooperativity chain visualization: NetworkX [@hagberg2008networkx]/matplotlib [@hunter2007matplotlib] for basic networks and GraphViz [@graphviz2024] for publication-quality graphics with customizable layouts and high-resolution export (PNG, SVG, PDF).

**Dual Interface Design**: Both graphical interface (tkinter [@tkinter2024]-based) and command-line interface with extensive parameter customization and preset configurations for different structure types (high-resolution, NMR, membrane proteins). In addition, users can use HBAT's API interfaces to build custom scientific workflows and perform analysis using scientific notebooks.

**Flexible Output Formats**: Multiple export options including text, CSV, and JSON formats for integration with downstream analysis pipelines and statistical software.

**Parameter Presets**: Built-in parameter sets optimized for different experimental conditions and structure types, addressing the challenge of parameter selection for different research contexts.

**PDB Fixing Integration**: Optional integration with PDBFixer for automated correction of missing hydrogen atoms, addressing a common preprocessing requirement.

![An example visualization of potential cooperativity chain generated by HBAT software for Protein Data Bank (PDB) entry 6RSA](https://static.abhishek-tiwari.com/hbat/6rsa-pdb-chain-6.png)

# Implementation

HBAT employs a modular architecture with separate components for PDB parsing, geometric analysis, statistical computation, and visualization. The core analysis engine uses efficient nearest-neighbor searching with configurable distance cutoffs, followed by geometric filtering based on distance and angular criteria.

The software implements the same fundamental geometric approach as the original version [@tiwari2007hbat] but with optimized algorithms and improved numerical precision.

Statistical analysis includes frequency distributions, geometric parameter distributions, and furcation analysis (bifurcated, trifurcated interactions). The cooperativity analysis uses graph theory to identify hydrogen bond networks and chains, with visualization support for understanding complex interaction patterns.

# Impact and Applications

Since its original publication, HBAT has been cited in numerous studies of protein structure and molecular recognition [@tiwari2007hbat]. Latest update, however, extends this impact by providing modern tools for:

- **Structure-based drug design**: Analysis of protein-ligand interactions and binding site characterization
- **Protein engineering**: Understanding stabilization mechanisms and rational design strategies  
- **Molecular dynamics analysis**: Post-simulation analysis of interaction networks and stability
- **Crystallographic studies**: Validation and interpretation of high-resolution structures
- **Comparative structural analysis**: Systematic comparison of interaction patterns across protein families

The software's preset configurations and flexible parameter system make it accessible to both computational experts and experimental structural biologists, broadening its potential user base compared to the original version.

# Availability and Future Development

HBAT is freely available under the MIT license at https://github.com/abhishektiwari/hbat with comprehensive documentation at https://hbat.abhishek-tiwari.com/. The software can be installed via PyPI (`pip install hbat`) or `conda`, with optional GraphViz integration for advanced visualization features.

Future development plans include support for trajectory analysis from molecular dynamics simulations, enhanced machine learning-based interaction classification, and integration with structural biology workflows through plugins for popular molecular visualization software.

# Acknowledgements

The author thanks the original co-developer Sunil K. Panigrahi and acknowledges the structural biology community for feedback that guided the modernization of HBAT.

# References