Using sketch-map coordinates to analyze and bias molecular dynamics simulations
Open accessAbstract
When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about the process of interest, serve this purpose. Because finding these CVs is the most difficult part of any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can be used to build a low-dimensional map of a phase space of high-dimensionality. In this paper we discuss how these machine-generated CVs can be used to accelerate the exploration of phase space and to reconstruct free-energy landscapes. To do so, we develop a formalism in which high-dimensional configurations are no longer represented by low-dimensional position vectors. Instead, for each configuration we calculate a probability distribution, which has a domain that encompasses the entirety of the low-dimensional space. To construct a biasing potential, we exploit an analogy with metadynamics and use the trajectory to adaptively construct a repulsive, history-dependent bias from the distributions that correspond to the previously visited configurations. This potential forces the system to explore more of phase space by making it desirable to adopt configurations whose distributions do not overlap with the bias. We apply this algorithm to a small model protein and succeed in reproducing the free-energy surface that we obtain from a parallel tempering calculation.
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The challenge of identifying the most appropriate low-dimensional space to accelerate conformational sampling is reflected in the fact that most early (and recent) applications of dimensionality reduction to atomistic modeling involved simulations of rare events or phase transitions.
“The challenge of identifying the most appropriate low-dimensional space to accelerate conformational sampling is reflected in the fact that most of early (and recent) applications of dimensionality reduction to atomistic modeling involved simulations of rare events or phase transitions 71,72,95-98 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [72]
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- Authors
- Tribello, G. A., Ceriotti, M., & Parrinello, M.
- Affiliations
- ETH Zurich (CH)
- University of Oxford (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-03-16
- Volume
- 109
- Issue
- 14
- Pages
- 5196-5201
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3325650
- OpenAlex
- W2038458884
- Cited by
- 202
- References
- 48
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 5 · 2023: 9 · 2022: 19 · 2021: 20 · 2020: 18 · 2019: 16 · 2018: 12 · 2017: 40 · 2016: 15 · 2015: 13 · 2014: 10 · 2013: 5 · 2012: 3
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Research Portal (Queen's University Belfast) (submittedVersion, other-oa)
- Funders
- FP7 Ideas: European Research Council (247075)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- sketch maps, dimensionality reduction, collective variables, free energy landscape, biasing potential, metadynamics, phase space exploration, protein folding, coarse-grained models, parallel tempering
- MeSH terms
- Probability, Protein Folding, Molecular Dynamics Simulation
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