ArticleScientific reports2017
GRAFENE: Graphlet-based alignment-free network approach integrates 3D structural and sequence (residue order) data to improve protein structural comparison.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 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
- Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer.NAR genomics and bioinformatics · 2024Article
- Protein remote homology detection and structural alignment using deep learning.Nature biotechnology · 2024Article
- Freeprotmap: waiting-free prediction method for protein distance map.BMC bioinformatics · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Metrics for network comparison using egonet feature distributions.Scientific reports · 2023Article
- Sequence-structure-function relationships in the microbial protein universe.Nature communications · 2023Article
- Graph embedding and geometric deep learning relevance to network biology and structural chemistry.Frontiers in artificial intelligence · 2023Review
- Multi-layer sequential network analysis improves protein 3D structural classification.Proteins · 2022Article
- Understanding structural variability in proteins using protein structural networks.Current research in structural biology · 2022Article
- Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein-Protein Binding.Frontiers in bioinformatics · 2021Article
- Network analysis of synonymous codon usage.Bioinformatics (Oxford, England) · 2020Article
- DeeplyEssential: a deep neural network for predicting essential genes in microbes.BMC bioinformatics · 2020Article
- Chromatin network markers of leukemia.Bioinformatics (Oxford, England) · 2020Article
- Network-based protein structural classification.Royal Society open science · 2020Article
- Comparing methods for comparing networks.Scientific reports · 2019Article
- Functional geometry of protein interactomes.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Initial protein structural comparisons were sequence-based. Since amino acids that are distant in the sequence can be close in the 3-dimensional (3D) structure, 3D contact approaches can complement sequence approaches. Traditional 3D contact approaches study 3D structures directly and are alignment-based. Instead, 3D structures can be modeled as protein structure networks (PSNs). Then, network approaches can compare proteins by comparing their PSNs. These can be alignment-based or alignment-free. We focus on the latter. Existing network alignment-free approaches have drawbacks: 1) They rely on naive measures of network topology. 2) They are not robust to PSN size. They cannot integrate 3) multiple PSN measures or 4) PSN data with sequence data, although this could improve comparison because the different data types capture complementary aspects of the protein structure. We address this by: 1) exploiting well-established graphlet measures via a new network alignment-free approach, 2) introducing normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary PSN data and sequence (specifically, residue order) data. We compare synthetic networks and real-world PSNs more accurately and faster than existing network (alignment-free and alignment-based), 3D contact, or sequence approaches.
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Registered trials
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