ArticleRoyal Society open science2020
Network-based protein structural classification.
Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context.Bioinformatics (Oxford, England) · 2026Article
- The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures.Bioinformatics advances · 2026Article
- An egonet based approach to effective weighted network comparison.Scientific reports · 2025Article
- Transcription factor prediction using protein 3D secondary structures.Bioinformatics (Oxford, England) · 2024Article
- Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer.NAR genomics and bioinformatics · 2024Article
- Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches.International journal of molecular sciences · 2024Review
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Multi-layer sequential network analysis improves protein 3D structural classification.Proteins · 2022Article
- Network analysis of synonymous codon usage.Bioinformatics (Oxford, England) · 2020Article
- Network-based protein structural classification.Royal Society open science · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.