ArticleProtein science : a publication of the Protein Society2016
Network representation of protein interactions: Theory of graph description and analysis.
Article in Protein science : a publication of the Protein Society, 2016. 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.
- Free energy perturbations in enzyme kinetic models reveal cryptic epistasis.PLoS computational biology · 2026Article
- The immune duality of osteopontin and its therapeutic implications for kidney transplantation.Frontiers in immunology · 2025Review
- Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution.Nature communications · 2023Article
- Effects of pH on an IDP conformational ensemble explored by molecular dynamics simulation.Biophysical chemistry · 2021Article
- Biological network analysis with deep learning.Briefings in bioinformatics · 2021Review
- Protein conformational switch discerned via network centrality properties.Computational and structural biotechnology journal · 2021Article
- Chokepoints in Mechanical Coupling Associated with Allosteric Proteins: The Pyruvate Kinase Example.Biophysical journal · 2019Article
- Molecular Dynamics Simulations of Kir2.2 Interactions with an Ensemble of Cholesterol Molecules.Biophysical journal · 2018Article
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Abstract
A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin.
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