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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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Cites
The PyMOL Molecular Graphics System
DeLano, W. L. (2002) · Medical Entomology and Zoology · Cited by 18,147
GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. R. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700301q · Cited by 15,830
Wang, J., Cieplak, P., & Kollman, P. A. (2000) · Journal of Computational Chemistry · doi:10.1002/1096-987x(200009)21:12 · Cited by 4,048
Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305
Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families
Lockless, S. W., & Ranganathan, R. (1999) · Science · doi:10.1126/science.286.5438.295 · Cited by 1,288
Csermely, P., Palotai, R., & Nussinov, R. (2010) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2010.04.009 · Cited by 821
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
LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins
Hendlich, M., Rippmann, F., & Barnickel, G. (1997) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(98)00002-3 · Cited by 732
Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
Buch, I., Giorgino, T., & De Fabritiis, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1103547108 · Cited by 655
Conformational selection or induced fit: A flux description of reaction mechanism
Hammes, G. G., Chang, Y.-C., & Oas, T. G. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907195106 · Cited by 577
Exploiting a natural conformational switch to engineer an interleukin-2 ‘superkine’
Levin, A. M., Bates, D. L., Ring, A. M., Krieg, C., Lin, J. T., Su, L., Moraga, I., Raeber, M. E., Bowman, G. R., Novick, P. A., et al. (2012) · Nature · doi:10.1038/nature10975 · Cited by 562
Self-citationScreen Savers of the World Unite!
Shirts, M., & Pande, V. (2000) · Science · doi:10.1126/science.290.5498.1903 · Cited by 550
Molecular structure of the acyl-enzyme intermediate in β-lactam hydrolysis at 1.7 Å resolution
Strynadka, N. C. J., Adachi, H., Jensen, S. E., Johns, K. L., Sielecki, A. R., Betzel, C., Sutoh, K., & James, M. N. G. (1992) · Nature · doi:10.1038/359700a0 · Cited by 525
Site-directed ligand discovery
Erlanson, D. A., Braisted, A. C., Raphael, D. R., Randal, M., Stroud, R. M., Gordon, E. M., & Wells, J. A. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.17.9367 · Cited by 466
Evolution of an Antibiotic Resistance Enzyme Constrained by Stability and Activity Trade-offs
Wang, X., Minasov, G., & Shoichet, B. K. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00400-x · Cited by 462
Transition networks for modeling the kinetics of conformational change in macromolecules.
Noé, F., & Fischer, S. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.01.008 · Cited by 450
Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR
Tang, C., Schwieters, C. D., & Clore, G. M. (2007) · Nature · doi:10.1038/nature06232 · Cited by 416
MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories
Schmidtke, P., Bidon‐Chanal, A., Luque, F. J., & Barril, X. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr550 · Cited by 398
Young, M. A., Gonfloni, S., Superti‐Furga, G., Roux, B., & Kuriyan, J. (2001) · Cell · doi:10.1016/s0092-8674(01)00301-4 · Cited by 386
Binding of small molecules to an adaptive protein–protein interface
Arkin, M. R., Randal, M., DeLano, W. L., Hyde, J., Luong, T. N., Oslob, J. D., Raphael, D. R., Taylor, L., Wang, J., McDowell, R. S., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.252756299 · Cited by 376
MSMBuilder2: Modeling Conformational Dynamics on the Picosecond to Millisecond Scale
Beauchamp, K. A., Bowman, G. R., Lane, T. J., Maibaum, L., Haque, I. S., & Pande, V. S. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct200463m · Cited by 372
Self-citationDiscovery of a Novel Binding Trench in HIV Integrase
Schames, J. R., Henchman, R. H., Siegel, J. S., Sotriffer, C., Ni, H., & McCammon, J. A. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0341913 · Cited by 354
Searching for new allosteric sites in enzymes
Hardy, J. A., & Wells, J. A. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.10.009 · Cited by 326
Using generalized ensemble simulations and Markov state models to identify conformational states
Bowman, G., Huang, X.-H., & Pande, V. (2009) · Methods · doi:10.1016/j.ymeth.2009.04.013 · Cited by 301
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Ceccarelli, D. F. J., Tang, X., Pelletier, B., Orlicky, S. M., Xie, W., Plantevin, V., Neculai, D., Chou, Y.-C., Ogunjimi, A. A., Al-Hakim, A., et al. (2011) · Cell · doi:10.1016/j.cell.2011.05.039 · Cited by 241
A Role for Both Conformational Selection and Induced Fit in Ligand Binding by the LAO Protein
Silva, D., Bowman, G. R., Sosa‐Peinado, A., & Huang, X. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002054 · Cited by 229
Self-citationIntramolecular Signaling Pathways Revealed by Modeling Anisotropic Thermal Diffusion
Ota, N., & Agard, D. A. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.05.043 · Cited by 227
Quantifying Correlations Between Allosteric Sites in Thermodynamic Ensembles
McClendon, C. L., Friedland, G. D., Mobley, D. L., Amirkhani, H., & Jacobson, M. P. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9001812 · Cited by 220
Protein folded states are kinetic hubs
Bowman, G., & Pande, V. (2010) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1003962107 · Cited by 216
Self-citationHilser, V. J., Dowdy, D. W., Oas, T. G., & Freire, E. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.17.9903 · Cited by 215
Transient Pockets on Protein Surfaces Involved in Protein−Protein Interaction
Eyrisch, S., & Helms, V. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm070095g · Cited by 212
Pan, H. C., Lee, J. C., & Hilser, V. J. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.220240297 · Cited by 201
Włodarski, T., & Žagrović, B. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906966106 · Cited by 194
Novel Allosteric Sites on Ras for Lead Generation
Grant, B. J., Lukman, S., Hocker, H. J., Sayyah, J., Brown, J. H., McCammon, J. A., & Gorfe, A. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025711 · Cited by 180
Automated electron‐density sampling reveals widespread conformational polymorphism in proteins
Lang, P. T., Ng, H. L., Fraser, J. S., Corn, J. E., Echols, N., Sales, M., Holton, J. M., & Alber, T. (2010) · Protein Science · doi:10.1002/pro.423 · Cited by 171
Reactive flux and folding pathways in network models of coarse-grained protein dynamics
Berezhkovskii, A. M., Hummer, G., & Szabó, A. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3139063 · Cited by 169
Markov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD Trajectories
Lane, T. J., Bowman, G. R., Beauchamp, K. A., Voelz, V. A., & Pande, V. S. (2011) · Journal of the American Chemical Society · doi:10.1021/ja207470h · Cited by 168
Self-citationTurning a protein kinase on or off from a single allosteric site via disulfide trapping
Sadowsky, J., Burlingame, M. A., Wolan, D. W., McClendon, C. L., Jacobson, M. P., & Wells, J. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102376108 · Cited by 157
Allosteric Inhibition Through Core Disruption
Horn, J. R., & Shoichet, B. K. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.12.068 · Cited by 150
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Lundqvist, T., Fisher, S. L., Kern, G., Folmer, R. H. A., Xue, Y., Newton, D. T., Keating, T. A., Alm, R. A., & DeJonge, B. L. M. (2007) · Nature · doi:10.1038/nature05689 · Cited by 142
Hilser, V. J. (2010) · Science · doi:10.1126/science.1186121 · Cited by 141
Atomistic folding simulations of the five helix bundle protein λ6–85
Bowman, G., Voelz, V. A., & Pande, V. (2011) · Journal of the American Chemical Society · doi:10.1021/ja106936n · Cited by 139
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Wrabl, J. O., Gu, J., Liu, T., Schrank, T. P., Whitten, S. T., & Hilser, V. J. (2011) · Biophysical Chemistry · doi:10.1016/j.bpc.2011.05.020 · Cited by 134
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Lau, A. Y., & Roux, B. (2011) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2010 · Cited by 120
Yang, S., Banavali, N. K., & Roux, B. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808261106 · Cited by 115
Bucher, D., Grant, B. J., & McCammon, J. A. (2011) · Biochemistry · doi:10.1021/bi201481a · Cited by 103
Frembgen-Kesner, T., & Elcock, A. H. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.03.021 · Cited by 98
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Morcos, F., Chatterjee, S., McClendon, C. L., Brenner, P. R., López-Rendón, R., Zintsmaster, J. S., Ercsey-Ravasz, M., Sweet, C. R., Jacobson, M. P., Peng, J. W., et al. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001015 · Cited by 89
Liang, M. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg553 · Cited by 84
Mechanisms of protein–ligand association and its modulation by protein mutations
Held, M., Metzner, P., Prinz, J.-H., & Noé, F. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2010.12.3699 · Cited by 77
DuBay, K. H., Bothma, J. P., & Geissler, P. L. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002168 · Cited by 65
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Bowman, G. R., Huang, X., & Pande, V. S. (2010) · Cell Research · doi:10.1038/cr.2010.57 · Cited by 56
Self-citationGrimsley, J. K., Calamini, B., Wild, J. R., & Mesecar, A. D. (2005) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2005.08.012 · Cited by 49
DuBay, K. H., & Geissler, P. L. (2009) · Journal of Molecular Biology · doi:10.1016/j.jmb.2009.05.068 · Cited by 46
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Lenaerts, T., Ferkinghoff‐Borg, J., Stricher, F., Serrano, L. G., Schymkowitz, J. W. H., & Rousseau, F. (2008) · BMC Structural Biology · doi:10.1186/1472-6807-8-43 · Cited by 40
Gee, C. L., Drinkwater, N., Tyndall, J. D. A., Grunewald, G. L., Wu, Q., McLeish, M. J., & Martin, J. L. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm0703385 · Cited by 24
Brandman, R., Lampe, J. N., Brandman, Y., & de Montellano, P. R. O. (2011) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2011.02.020 · Cited by 20
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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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