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Updated: Oct 28, 2025

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Atom-specific persistent homology and its application to protein flexibility analysis.

David Bramer1, Guo-Wei Wei2

  • 1Department of Mathematics, Michigan State University, MI 48824, USA.

Computational and Mathematical Biophysics
|July 19, 2021
PubMed
Summary

This study introduces atom-specific persistent homology, a novel method to analyze local atomic properties in molecules. This topological approach enhances biomolecular flexibility and B-factor prediction using machine learning.

Keywords:
Atom-specific topologyConvolutional neural networkElement-specific persistent homologyGradient boosting treeProtein flexibility

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Area of Science:

  • Computational Biology
  • Topological Data Analysis
  • Structural Bioinformatics

Background:

  • Persistent homology is successful in analyzing biomolecular data by examining atomic connectivity at various scales.
  • However, its application to local atomic properties like flexibility and B-factor prediction is limited.

Purpose of the Study:

  • To introduce atom-specific persistent homology for local atomic-level molecular representation.
  • To apply this method for analyzing and predicting localized properties in macromolecules.

Main Methods:

  • Developed atom-specific persistent homology using conjugated sets of atoms and simplicial complexes.
  • Utilized Bottleneck and Wasserstein metrics to measure differences in topological invariants.
  • Integrated atom-specific topological features with machine learning algorithms (gradient boosting trees, CNN).

Main Results:

  • The proposed method provides localized atomic-level topological representations.
  • Successfully applied to protein thermal fluctuation analysis and B-factor prediction.
  • Demonstrated effectiveness in analyzing and predicting localized information in macromolecules.

Conclusions:

  • Atom-specific persistent homology offers a powerful new topological tool for biomolecular analysis.
  • This method enables the prediction of localized atomic properties previously difficult to assess.
  • The approach integrates global topological tools with local property analysis effectively.