ArticleInternational journal for numerical methods in biomedical engineering2014
Persistent homology analysis of protein structure, flexibility, and folding.
Article in International journal for numerical methods in biomedical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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92 citing papers in PubMed, 243 citations in OpenAlex.
- Topology-enhanced machine learning for speech signal processing.Nature communications · 2026Article
- Topological potentials guiding protein self-assembly.PLoS computational biology · 2026Article
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- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Persistent Stanley-Reisner Theory.Foundations of data science (Springfield, Mo.) · 2026Article
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
- Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real-World Data.Advanced intelligent discovery · 2025Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- PERSISTENT DIRECTED FLAG LAPLACIAN.Foundations of data science (Springfield, Mo.) · 2025Article
- Superionic Ionic Conductor Discovery via Multiscale Topological Learning.Journal of the American Chemical Society · 2025Article
- Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains.Journal of chemical theory and computation · 2025Article
- A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity.Scientific reports · 2025Article
- Faithful Interpretation of Protein Structures through Weighted Persistent Homology Improves Evolutionary Distance Estimation.Molecular biology and evolution · 2025Article
- Persistent Mayer Dirac.Journal of physics. Complexity · 2024Article
- Poisson-Boltzmann-based machine learning model for electrostatic analysis.Biophysical journal · 2024Article
- Topological Learning Approach to Characterizing Biological Membranes.Journal of chemical information and modeling · 2024Article
- Position: Topological Deep Learning is the New Frontier for Relational Learning.Proceedings of machine learning research · 2024Article
- A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.Scientific reports · 2024Article
32 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Proteins are the most important biomolecules for living organisms. The understanding of protein structure, function, dynamics, and transport is one of the most challenging tasks in biological science. In the present work, persistent homology is, for the first time, introduced for extracting molecular topological fingerprints (MTFs) based on the persistence of molecular topological invariants. MTFs are utilized for protein characterization, identification, and classification. The method of slicing is proposed to track the geometric origin of protein topological invariants. Both all-atom and coarse-grained representations of MTFs are constructed. A new cutoff-like filtration is proposed to shed light on the optimal cutoff distance in elastic network models. On the basis of the correlation between protein compactness, rigidity, and connectivity, we propose an accumulated bar length generated from persistent topological invariants for the quantitative modeling of protein flexibility. To this end, a correlation matrix-based filtration is developed. This approach gives rise to an accurate prediction of the optimal characteristic distance used in protein B-factor analysis. Finally, MTFs are employed to characterize protein topological evolution during protein folding and quantitatively predict the protein folding stability. An excellent consistence between our persistent homology prediction and molecular dynamics simulation is found. This work reveals the topology-function relationship of proteins.
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