Simple few-state models reveal hidden complexity in protein folding
Open accessAbstract
Markov state models constructed from molecular dynamics simulations have recently shown success at modeling protein folding kinetics. Here we introduce two methods, flux PCCA+ (FPCCA+) and sliding constraint rate estimation (SCRE), that allow accurate rate models from protein folding simulations. We apply these techniques to fourteen massive simulation datasets generated by Anton and Folding@home. Our protocol quantitatively identifies the suitability of describing each system using two-state kinetics and predicts experimentally detectable deviations from two-state behavior. An analysis of the villin headpiece and FiP35 WW domain detects multiple native substates that are consistent with experimental data. Applying the same protocol to GTT, NTL9, and protein G suggests that some beta containing proteins can form long-lived native-like states with small register shifts. Even the simplest protein systems show folding and functional dynamics involving three or more states.
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Recent methods such as Bayesian agglomerative clustering, hierarchical Nystrom, flux PCCA+, and HMM-based coarse-graining have proposed clustering methods to address statistical reliability.
“Recent methods such as the Bayesian agglomerative clustering (BACE) method [33], a hierarchical Nystrom method [34], flux PCCA+ [35] and HMM-based coarse-graining [3] have proposed clustering methods to address this statistical reliability issue.”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [35]
The Abstract and FPCCA+ section (pages not available in our copy) describe flux PCCA+ as a method for constructing macrostates from microstates. They say its purpose is to retain relevant slow dynamics while excluding low-population modes, not specifically to address statistical reliability. The Model Construction section discusses reliable statistics in choosing the number of microstate clusters, but does not identify that as the problem FPCCA+ solves. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAn examination of fourteen massive protein simulation datasets suggested significant, experimentally detectable deviations from the two-state behavior generally believed to describe folding of many simple model protein systems, suggesting previously unappreciated complexity.
“Even more surprisingly, an examination of fourteen massive protein simulation datasets suggested that there are significant, experimentally detectable deviations from the two-state behavior generally believed to describe the folding of many simple model protein systems, suggesting a previously unappreciated complexity [35].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [35]
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References 63 · Cited by 161
Research works printed but not linked: 3
- Shaw D, et al. (2008) Anton, a special-purpose machine for molecular dynamics simu- lation. Commun ACM 51:91-97.
- Best R, Hummer G (2005) Reaction coordinates and rates from transition paths. Proc. Natl. Acad. Sci USA 102:6732-6737.
- Mullner D (2011) Modern hierarchical, agglomerative clustering algorithms. Arxiv.
- Authors
- Beauchamp, K. A., McGibbon, R., Lin, Y.-S., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-07-09
- Volume
- 109
- Issue
- 44
- Pages
- 17807-17813
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3497769
- OpenAlex
- W2068540815
- Cited by
- 161
- References
- 63
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 7 · 2022: 10 · 2021: 13 · 2020: 7 · 2019: 15 · 2018: 8 · 2017: 17 · 2016: 10 · 2015: 18 · 2014: 20 · 2013: 23 · 2012: 2
- FWCI
- 8.09
- 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
- National Science Foundation (0954714, 0900700, 0619926)
- National Institutes of Health (NIH-R01)
- National Institute of General Medical Sciences (R01GM062868, NIH R01-GM062868)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Keywords
- protein folding kinetics, Markov state models, villin headpiece, protein G
- MeSH terms
- Kinetics, Microfilament Proteins, Models, Molecular, Models, Theoretical, Proteins, Protein Folding
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