Protein Folding Is Mechanistically Robust
Open accessAbstract
Markov state models (MSMs) have proven to be useful tools in simulating large and slowly-relaxing biological systems like proteins. MSMs model proteins through dynamics on a discrete-state energy landscape, allowing molecules to effectively sample large regions of phase space. In this work, we use aspects of MSMs to ask: is protein folding mechanistically robust? We first provide a definition of mechanism in the context of Markovian models, and we later use perturbation theory and the concept of parametric sloppiness to show that parts of the MSM eigenspectrum are resistant to perturbation. We introduce a new, to our knowledge, Bayesian metric by which eigenspectrum robustness can be evaluated, and we discuss the implications of mechanistic robustness and possible new applications of MSMs to understanding biophysical phenomena.
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The dominant experimentally observable slow processes appear to be robust to perturbations in intrinsic rates expected from variations in experimental conditions, suggesting this behavior should be relatively universally observable.
“The dominant experimentally observable slow processess appear to also be robust to perturbations in intrinsic rates that would be expected from variations in experimental conditions, suggesting this behavior should be relatively universally observable [50].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [50]
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Markov models of molecular kinetics: Generation and validation
Prinz, J.-H., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J. D., Schütte, C., & Noé, F. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3565032 · Cited by 1,275 · see citing works
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261 · see citing works
The Dynamic Energy Landscape of Dihydrofolate Reductase Catalysis
Boehr, D. D., McElheny, D., Dyson, H. J., & Wright, P. E. (2006) · Science · doi:10.1126/science.1130258 · Cited by 958 · see citing works
Principles of protein folding, misfolding and aggregation
Dobson, C. M. (2004) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2003.12.008 · Cited by 900 · see citing works
How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization?
Piana, S., Lindorff‐Larsen, K., & Shaw, D. E. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.03.051 · Cited by 798 · see citing works
Everything you wanted to know about Markov State Models but were afraid to ask
Pande, V. S., Beauchamp, K., & Bowman, G. R. (2010) · Methods · doi:10.1016/j.ymeth.2010.06.002 · Cited by 698 · see citing works
Self-citationTwo-State Allosteric Behavior in a Single-Domain Signaling Protein
Volkman, B. F., Lipson, D., Wemmer, D. E., & Kern, D. (2001) · Science · doi:10.1126/science.291.5512.2429 · Cited by 594 · see citing works
Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1–39)
Voelz, V. A., Bowman, G., Beauchamp, K., & Pande, V. (2010) · Journal of the American Chemical Society · doi:10.1021/ja9090353 · Cited by 529 · see citing works
Self-citationDescribing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory
Swope, W., Pitera, J., & Suits, F. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp037421y · Cited by 450 · see citing works
Ten-Microsecond Molecular Dynamics Simulation of a Fast-Folding WW Domain
Freddolino, L., Liu, F., Gruebele, M., & Schulten, K. J. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.131565 · Cited by 336 · see citing works
Gupta, N. K., & Mehra, R. К. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100714 · Cited by 266 · see citing works
Long‐Time Protein Folding Dynamics from Short‐Time Molecular Dynamics Simulations
Chodera, J. D., Swope, W. C., Pitera, J. W., & Dill, K. A. (2006) · Multiscale Modeling and Simulation · doi:10.1137/06065146x · Cited by 220 · see citing works
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 · see citing works
Self-citationKaranicolas, J., & Brooks, C. L. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.09.047 · Cited by 214 · see citing works
Mapping the Folding Pathway of an Immunoglobulin Domain
Fowler, S. A., & Clarke, J. (2001) · Structure · doi:10.1016/s0969-2126(01)00596-2 · Cited by 198 · see citing works
Sloppiness, robustness, and evolvability in systems biology
Daniels, B. C., Chen, Y.-J., Sethna, J. P., Gutenkunst, R. N., & Myers, C. R. (2008) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2008.06.008 · Cited by 192 · see citing works
Error analysis and efficient sampling in Markovian state models for molecular dynamics.
Singhal, N., & Pande, V. (2005) · Journal of Chemical Physics · doi:10.1063/1.2116947 · Cited by 175 · see citing works
Self-citationReactive 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 170 · see citing works
Sloppy-model universality class and the Vandermonde matrix.
Waterfall, J. J., Casey, F. P., Gutenkunst, R. N., Brown, K. S., Myers, C. R., Brouwer, P. W., Elser, V., & Sethna, J. P. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.150601 · Cited by 164 · see citing works
Rapid equilibrium sampling initiated from nonequilibrium data
Huang, X.-H., Bowman, G., Bacallado, S., & Pande, V. (2009) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0909088106 · Cited by 155 · see citing works
Self-citationAtomistic 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 · see citing works
Self-citationProbability distributions of molecular observables computed from Markov models
Noé, F. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2916718 · Cited by 135 · see citing works
Matthews, J. M., & Fersht, A. R. (1995) · Biochemistry · doi:10.1021/bi00020a027 · Cited by 131 · see citing works
Yang, S., Banavali, N. K., & Roux, B. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808261106 · Cited by 115 · see citing works
The Folding of Spectrin Domains II: Phi-value Analysis of R16
Scott, K. A., Randles, L. G., & Clarke, J. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.09.023 · Cited by 61 · see citing works
The Elements of Quantum Mechanics
(1959) · Pure and applied physics · doi:10.1016/b978-0-12-750550-3.50009-4 · Cited by 38 · see citing works
Principal Hessian Directions for regression with measurement error
Lue, H. (2004) · Biometrika · doi:10.1093/biomet/91.2.409 · Cited by 27 · see citing works
Robustness of protein folding kinetics to surface hydrophobic substitutions
Gu, H., Doshi, N., Kim, D. E., et al. (1999) · Protein Science · doi:10.1110/ps.8.12.2734 · Cited by 23 · see citing works
Berthod, C. (2018) · Institute of Physics eBooks · doi:10.1088/978-0-7503-1741-2ch2 · Cited by 9 · see citing works
Algorithms for building models of molecular motion from simulations
Pande, V. S., Batzoglou, S., & Hinrichs, N. (2007) · Cited by 4 · see citing works
Self-citation
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Research works printed but not linked: 1
- [7]7. Schutte, C. 1999. Conformational dynamics: modeling, theory, algorithm, and application to biomolecules. Habilitation thesis. Freie Universitat Berlin, Germany.
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Markov 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 · see citing works
- Authors
- Weber, J. K., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Corresponding author
- Pande, Vijay S.
- Venue
- Biophysical Journal
- Publisher
- Elsevier BV
- Published
- 2012-02
- Volume
- 102
- Issue
- 4
- Pages
- 859-867
- ISSN
- 0006-3495 (ISSN-L)
- PMC
- PMC3283819
- OpenAlex
- W2051821750
- Cited by
- 13
- References
- 30
- Citations by year
- 2022: 1 · 2021: 1 · 2020: 1 · 2018: 1 · 2017: 1 · 2015: 2 · 2014: 5 · 2012: 1
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Biophysical Journal (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM062868)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein folding, Markov state models, energy landscape, perturbation theory, Markov model
- MeSH terms
- Bayes Theorem, Markov Chains, Protein Folding
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