ArticlePloS one2013
Hot spots in a network of functional sites.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed, 39 citations in OpenAlex.
- MAXTIA: A high-throughput platform for rapid functional epitope mapping by kinetic screening of mutant libraries.Protein science : a publication of the Protein Society · 2026Article
- PPI-hotspoteLife · 2024Article
- Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation.The journal of physical chemistry. B · 2024Article
- Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease.Proteins · 2021Article
- Normal Mode Analysis of KRas4B Reveals Partner Specific Dynamics.The journal of physical chemistry. B · 2021Article
- Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Gaussian network model can be enhanced by combining solvent accessibility in proteins.Scientific reports · 2017Article
- Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case.BioMed research international · 2017Article
- A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.International journal of molecular sciences · 2016Article
- Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes.BioMed research international · 2016Article
- Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins.Bioinformatics (Oxford, England) · 2015Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.
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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.