The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites
Open accessAbstract
Cryptic allosteric sites—transient pockets in a folded protein that are invisible to conventional experiments but can alter enzymatic activity via allosteric communication with the active site—are a promising opportunity for facilitating drug design by greatly expanding the repertoire of available drug targets. Unfortunately, identifying these sites is difficult, typically requiring resource-intensive screening of large libraries of small molecules. Here, we demonstrate that Markov state models built from extensive computer simulations (totaling hundreds of microseconds of dynamics) can identify prospective cryptic sites from the equilibrium fluctuations of three medically relevant proteins—β-lactamase, interleukin-2, and RNase H—even in the absence of any ligand. As in previous studies, our methods reveal a surprising variety of conformations—including bound-like configurations—that implies a role for conformational selection in ligand binding. Moreover, our analyses lead to a number of unique insights. First, direct comparison of simulations with and without the ligand reveals that there is still an important role for an induced fit during ligand binding to cryptic sites and suggests new conformations for docking. Second, correlations between amino acid sidechains can convey allosteric signals even in the absence of substantial backbone motions. Most importantly, our extensive sampling reveals a multitude of potential cryptic sites—consisting of transient pockets coupled to the active site—even in a single protein. Based on these observations, we propose that cryptic allosteric sites may be even more ubiquitous than previously thought and that our methods should be a valuable means of guiding the search for such sites.
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“The intrinsic fluctuations of β-lactamase in its native state reveals a multitude of potential allosteric binding sites”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [61]
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“MSMs can predict kinetic quantities on long timescales (e.g. milliseconds) using a set of much shorter MD simulations.395–396”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [395]
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Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations
Noé, F., Schütte, C., Vanden-Eijnden, E., Reich, L., & Weikl, T. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905466106 · Cited by 820
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Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
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Conformational selection or induced fit: A flux description of reaction mechanism
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“The TICA problem can easily be solved by subsequently solving two simple eigenvalue problems.”
BackgroundMarkov state models of biomolecular conformational dynamics
Chodera, J. D., & Noé, F. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.04.002 · Cited by 776
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Identification of slow molecular order parameters for Markov model constructionPreprint
Perez-hern, G., Paul, F., Toni Giorgino, A., Fabritiis, G. D., Noé, F., & Contribution, E. (2013) · Cited by 657
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Metadata
- Authors
- Bowman, G. R., & Geissler, P. L.
- Affiliations
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-07-02
- Volume
- 109
- Issue
- 29
- Pages
- 11681-11686
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3406870
- OpenAlex
- W2043580883
- Cited by
- 279
- References
- 61
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 13 · 2023: 19 · 2022: 13 · 2021: 22 · 2020: 25 · 2019: 17 · 2018: 21 · 2017: 17 · 2016: 34 · 2015: 25 · 2014: 13 · 2013: 19
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM062868)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- Markov state models, conformational selection, induced fit, ligand binding, beta-lactamase, interleukin-2, RNase H, allosteric communication, protein conformational sampling
- MeSH terms
- Allosteric Site, beta-Lactamases, Interleukin-2, Models, Chemical, Protein Binding, Ribonuclease H, Protein Folding, Molecular Dynamics Simulation
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