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Cited by 39Protein Structure and DynamicsAdvanced Physical and Chemical Molecular InteractionsRNA and protein synthesis mechanisms

Delineation of Folding Pathways of a β-Sheet Miniprotein

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Zheng, Wenwei, Qi, Bo, Rohrdanz, Mary A., Caflisch, Amedeo, Dinner, Aaron R., Clementi, Cecilia·2011·The Journal of Physical Chemistry B
DOI 10.1021/jp2076935
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Abstract

Several methods have been developed in the past few years for the analysis of molecular dynamics simulations of biological (macro)molecules whose complexity is difficult to capture by simple projections of the free-energy surface onto one or two geometric variables. The locally scaled diffusion map (LSDMap) method is a nonlinear dimensionality reduction technique for describing the dynamics of complex systems in terms of a few collective coordinates. Here, we compare LSDMap to two previously developed approaches for the characterization of the configurational landscape associated with the folding dynamics of a three-stranded antiparallel β-sheet peptide, termed Beta3s. The analysis is aided by an improved procedure for extracting pathways from the equilibrium transition network, which enables calculation of pathway-specific cut-based free energy profiles. We find that the results from LSDMap are consistent with analysis based on transition networks and allow a coherent interpretation of metastable states and folding pathways in terms of different time scales of transitions between minima on the free energy projections.

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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 [97]

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Authors
Zheng, W., Qi, B., Rohrdanz, M. A., Caflisch, A., Dinner, A. R., & Clementi, C.
Affiliations
  • Rice University (US)
  • University of Chicago (US)
  • University of Zurich (CH)
Corresponding author
Clementi, Cecilia
ORCID
Venue
The Journal of Physical Chemistry B
Publisher
American Chemical Society (ACS)
Published
2011-10-17
Volume
115
Issue
44
Pages
13065-13074
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
DOI
10.1021/jp2076935
OpenAlex
W2330733193
Cited by
39
References
42
Citations by year
2022: 1 · 2021: 1 · 2020: 2 · 2019: 6 · 2018: 2 · 2017: 4 · 2016: 3 · 2015: 6 · 2014: 3 · 2013: 9 · 2012: 2
FWCI
1.81
Type
journal article
Language
en
Open access
Locations
  • The Journal of Physical Chemistry B (publishedVersion)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
  • Zurich Open Repository and Archive (University of Zurich)
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 pathways, nonlinear dimensionality reduction, transition networks, free energy landscape, metastable states, collective coordinates
MeSH terms
Models, Molecular, Peptides, Thermodynamics, Protein Structure, Secondary, Protein Folding

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