Mapping allosteric communications within individual proteins
Open accessAbstract
Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.
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AlphaFold, which predicts single ranked structures for a protein sequence, is unable to address directly allosteric mechanisms, which are based on populations of conformational states in ensembles.
“AlphaFold, which predicts single ranked structures for a protein sequence, is unable to address directly allosteric mechanisms, which are based on the populations of conformational states in the ensembles.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [109]
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AlphaFold, Artificial Intelligence (AI), and Allostery
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- Authors
- Wang, J., Jain, A., McDonald, L. R., Gambogi, C., Lee, A. L., & Dokholyan, N. V.
- Affiliations
- Pennsylvania State University (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Dokholyan, Nikolay V.
- Venue
- Nature Communications
- Published
- 2020-07-31
- Volume
- 11
- Issue
- 1
- Pages
- 3862-3862
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7395124
- OpenAlex
- W3046878556
- Cited by
- 201
- References
- 71
- Citations by year
- 2026: 23 · 2025: 38 · 2024: 42 · 2023: 36 · 2022: 27 · 2021: 29 · 2020: 6
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (5R01GM123247, 2R01GM114015, 1R35GM134864)
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1TR002014)
- Passan Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein allostery, CheY, protein structure
- MeSH terms
- Methyl-Accepting Chemotaxis Proteins, Algorithms, Allosteric Regulation, Allosteric Site, Animals, Enzyme Inhibitors, Escherichia coli, Humans, Software, Protein Structure, Secondary, Internet, Protein Interaction Mapping, Escherichia coli Proteins, Molecular Dynamics Simulation
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