Cited by 41Protein Structure and DynamicsGene Regulatory Network AnalysisRNA and protein synthesis mechanisms

Allosteric conformational ensembles have unlimited capacity for integrating information

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Biddle, John W, Martinez-Corral, Rosa, Wong, Felix, Gunawardena, Jeremy·2021·eLife

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Abstract

Integration of binding information by macromolecular entities is fundamental to cellular functionality. Recent work has shown that such integration cannot be explained by pairwise cooperativities, in which binding is modulated by binding at another site. Higher-order cooperativities (HOCs), in which binding is collectively modulated by multiple other binding events, appear to be necessary but an appropriate mechanism has been lacking. We show here that HOCs arise through allostery, in which effective cooperativity emerges indirectly from an ensemble of dynamically interchanging conformations. Conformational ensembles play important roles in many cellular processes but their integrative capabilities remain poorly understood. We show that sufficiently complex ensembles can implement any form of information integration achievable without energy expenditure, including all patterns of HOCs. Our results provide a rigorous biophysical foundation for analysing the integration of binding information through allostery. We discuss the implications for eukaryotic gene regulation, where complex conformational dynamics accompanies widespread information integration.

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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 [101]

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Authors
Biddle, J. W., Martinez-Corral, R., Wong, F., & Gunawardena, J.
Affiliations
  • Harvard University (US)
  • Broad Institute (US)
Corresponding author
Gunawardena, Jeremy
Venue
eLife
Published
2021-06-09
Volume
10
ISSN
2050-084X (ISSN-L)
OpenAlex
W3171773865
Cited by
41
References
102
Citations by year
2026: 7 · 2025: 5 · 2024: 5 · 2023: 9 · 2022: 12 · 2021: 3
FWCI
2.26
Type
journal article
Language
en
Open access
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (1462629, DGE1144152)
  • National Institutes of Health (GM122928)
  • European Molecular Biology Organization (ALTF683-2019)
  • James S. McDonnell Foundation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
allosteric regulation, conformational ensembles, conformational dynamics, gene expression regulation
MeSH terms
Machine Learning, Allosteric Regulation, Animals, Gene Expression Regulation, Models, Biological, Protein Binding, Protein Conformation, Biophysical Phenomena

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