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Cited by 279Protein Structure and DynamicsComputational Drug Discovery MethodsReceptor Mechanisms and Signaling

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

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Bowman, Gregory R., Geissler, Phillip L.·2012·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1209309109PMC PMC3406870
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Abstract

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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Metadata

Authors
Bowman, G. R., & Geissler, P. L.
Affiliations
  • University of California, Berkeley (US)
  • Lawrence Berkeley National Laboratory (US)
ORCID
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
DOI
10.1073/pnas.1209309109
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
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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