Cited by 340Protein Structure and DynamicsModel Reduction and Neural NetworksAdvanced Physical and Chemical Molecular Interactions

Simplifying the representation of complex free-energy landscapes using sketch-map

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Ceriotti, Michele, Tribello, Gareth A., Parrinello, Michele·2011·Proceedings of the National Academy of Sciences

Abstract

A new scheme, sketch-map, for obtaining a low-dimensional representation of the region of phase space explored during an enhanced dynamics simulation is proposed. We show evidence, from an examination of the distribution of pairwise distances between frames, that some features of the free-energy surface are inherently high-dimensional. This makes dimensionality reduction problematic because the data does not satisfy the assumptions made in conventional manifold learning algorithms We therefore propose that when dimensionality reduction is performed on trajectory data one should think of the resultant embedding as a quickly sketched set of directions rather than a road map. In other words, the embedding tells one about the connectivity between states but does not provide the vectors that correspond to the slow degrees of freedom. This realization informs the development of sketch-map, which endeavors to reproduce the proximity information from the high-dimensionality description in a space of lower dimensionality even when a faithful embedding is not possible.

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Crossref · 10.1073/pnas.1108486108

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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 .”

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Authors
Ceriotti, M., Tribello, G. A., & Parrinello, M.
Affiliations
  • University of Oxford (GB)
  • ETH Zurich (CH)
Venue
Proceedings of the National Academy of Sciences
Published
2011-07-05
Volume
108
Issue
32
Pages
13023-13028
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1980782498
Cited by
340
References
40
Citations by year
2026: 14 · 2025: 17 · 2024: 18 · 2023: 22 · 2022: 26 · 2021: 36 · 2020: 40 · 2019: 34 · 2018: 26 · 2017: 45 · 2016: 18 · 2015: 20 · 2014: 5 · 2013: 9 · 2012: 8
FWCI
3.11
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (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
  • National Science Foundation
  • Royal Society
  • European Commission (ERC-2009-AdG-247075)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (ERC-2009-AdG)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
Keywords
sketch maps, free energy landscape, dimensionality reduction, manifold learning, enhanced sampling, phase space exploration, molecular dynamics trajectories

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