ArticleJournal of the Royal Society, Interface2023
Homology of homologous knotted proteins.
Article in Journal of the Royal Society, Interface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Topological potentials guiding protein self-assembly.PLoS computational biology · 2026Article
- The topological properties of the protein universe.Nature communications · 2025Article
- Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains.Journal of chemical theory and computation · 2025Article
- Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment.Bulletin of mathematical biology · 2024Article
- Interpretable Structural Evaluation of Metal-Oxide Nanostructures in Scanning Transmission Electron Microscopy (STEM) Images via Persistent Homology.Nanomaterials (Basel, Switzerland) · 2024Article
- Article
- The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement.PLoS computational biology · 2023Article
- Homology of homologous knotted proteins.Journal of the Royal Society, Interface · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantification and classification of protein structures, such as knotted proteins, often requires noise-free and complete data. Here, we develop a mathematical pipeline that systematically analyses protein structures. We showcase this geometric framework on proteins forming open-ended trefoil knots, and we demonstrate that the mathematical tool, persistent homology, faithfully represents their structural homology. This topological pipeline identifies important geometric features of protein entanglement and clusters the space of trefoil proteins according to their depth. Persistence landscapes quantify the topological difference between a family of knotted and unknotted proteins in the same structural homology class. This difference is localized and interpreted geometrically with recent advancements in systematic computation of homology generators. The topological and geometric quantification we find is robust to noisy input data, which demonstrates the potential of this approach in contexts where standard knot theoretic tools fail.
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Registered trials
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