ArticleProteins2022
Circuit topology predicts pathogenicity of missense mutations.
Article in Proteins, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations.Cell reports methods · 2025Article
- Polyglutamine expansion induced dynamic misfolding of androgen receptor.Protein science : a publication of the Protein Society · 2025Article
- Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures.Nature methods · 2024Article
- Rapid prediction of thermodynamically destabilizing tyrosine phosphorylations in cancers.bioRxiv : the preprint server for biology · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Circuit Topology Approach for the Comparative Analysis of Intrinsically Disordered Proteins.Journal of chemical information and modeling · 2023Article
- Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.Current opinion in structural biology · 2023Review
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Authors and funding
3 authors.
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Abstract
The contact topology of a protein determines important aspects of the folding process. The topological measure of contact order has been shown to be predictive of the rate of folding. Circuit topology is emerging as another fundamental descriptor of biomolecular structure, with predicted effects on the folding rate. We analyze the residue-based circuit topological environments of 21 K mutations labeled as pathogenic or benign. Multiple statistical lines of reasoning support the conclusion that the number of contacts in two specific circuit topological arrangements, namely inverse parallel and cross relations, with contacts involving the mutated residue have discriminatory value in determining the pathogenicity of human variants. We investigate how results vary with residue type and according to whether the gene is essential. We further explore the relationship to a number of structural features and find that circuit topology provides nonredundant information on protein structures and pathogenicity of mutations. Results may have implications for the polymer physics of protein folding and suggest that "local" topological information, including residue-based circuit topology and residue contact order, could be useful in improving state-of-the-art machine learning algorithms for pathogenicity prediction.
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