ArticleComputational and mathematical biophysics2020
Atom-specific persistent homology and its application to protein flexibility analysis.
Article in Computational and mathematical biophysics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Predicting protein-nucleic acid flexibility using persistent sheaf Laplacians.Physical chemistry chemical physics : PCCP · 2026Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- The topological properties of the protein universe.Nature communications · 2025Article
- Persistent Sheaf Laplacian Analysis of Protein Flexibility.The journal of physical chemistry. B · 2025Article
- Multiscale Differential Geometry Learning for Protein Flexibility Analysis.Journal of computational chemistry · 2025Article
- Multi-scale Jones polynomial and persistent Jones polynomial for knot data analysis.AIMS mathematics · 2025Article
- Knot data analysis using multiscale Gauss link integral.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Tight basis cycle representatives for persistent homology of large biological data sets.PLoS computational biology · 2023Article
- Article
- Persistent Homology for RNA Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Topological data analysis in biomedicine: A review.Journal of biomedical informatics · 2022Review
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2 authors.
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Abstract
Recently, persistent homology has had tremendous success in biomolecular data analysis. It works by examining the topological relationship or connectivity of a group of atoms in a molecule at a variety of scales, then rendering a family of topological representations of the molecule. However, persistent homology is rarely employed for the analysis of atomic properties, such as biomolecular flexibility analysis or B-factor prediction. This work introduces atom-specific persistent homology to provide a local atomic level representation of a molecule via a global topological tool. This is achieved through the construction of a pair of conjugated sets of atoms and corresponding conjugated simplicial complexes, as well as conjugated topological spaces. The difference between the topological invariants of the pair of conjugated sets is measured by Bottleneck and Wasserstein metrics and leads to an atom-specific topological representation of individual atomic properties in a molecule. Atom-specific topological features are integrated with various machine learning algorithms, including gradient boosting trees and convolutional neural network for protein thermal fluctuation analysis and B-factor prediction. Extensive numerical results indicate the proposed method provides a powerful topological tool for analyzing and predicting localized information in complex macromolecules.
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