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Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

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Das, Payel, Moll, Mark, Stamati, Hernán, Kavraki, Lydia E., Clementi, Cecilia·2006·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.0603553103PMC PMC1502548

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Abstract

The definition of reaction coordinates for the characterization of a protein-folding reaction has long been a controversial issue, even for the “simple” case in which one single free-energy barrier separates the folded and unfolded ensemble. We propose a general approach to this problem to obtain a few collective coordinates by using nonlinear dimensionality reduction. We validate the usefulness of this method by characterizing the folding landscape associated with a coarse-grained protein model of src homology 3 as sampled by molecular dynamics simulations. The folding free-energy landscape projected on the few relevant coordinates emerging from the dimensionality reduction can correctly identify the transition-state ensemble of the reaction. The first embedding dimension efficiently captures the evolution of the folding process along the main folding route. These results clearly show that the proposed method can efficiently find a low-dimensional representation of a complex process such as protein folding.

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Introduction to Algorithms
Adhi Harmoko, S., M.Komp, Jacquard, J. M., Zuse, K., & Eniac (2005) · Cited by 18,035
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Iterative Methods for Sparse Linear Systems
Saad, Y. E.-M. (2003) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718003 · Cited by 13,931
Eigenfaces for Recognition
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
Principal Component Analysis
Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8 · Cited by 8,481
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
Face recognition using eigenfaces
Turk, M., & Pentland, A. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1991.139758 · Cited by 5,262
ARPACK Users' Guide
Lehoucq, R. B., Sorensen, D. C., & Yang, C. (1998) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719628 · Cited by 2,938

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A. Ma, A. Dinner J. Phys. B.. 109, 6769–6779 (2005).
Y. Rhee, V. Pande J. Phys. B.. 109, 6780–6786 (2005).
10.1021/ja049510 · DOI
Y. Levy, A. Caflisch J. Phys. B.. 107, 3068–3079 (2003).
K. Q. Weinberger L. K. Saul (IEEE Washington DC) pp. 988–995 (2004).
V. de Silva, J. Tenenbaum Sparse Multidimensional Scaling Using Landmark Points. (Stanford Univ., Stanford, CA, 2004).
A. Ferrenberg, R. Swendsen Phys. Rev. Lett.. 63, 1185–1198 (1989).

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Authors: Das, P., Moll, M., Stamati, H., Kavraki, L. E., & Clementi, C.
Affiliations: Departments of *Chemistry,
Computer Science, and
Baylor College of Medicine (US)
Rice University (US)
ORCID: Das, Payel
Moll, Mark
Kavraki, Lydia E.
Clementi, Cecilia
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2006-06-27
Volume: 103
Issue: 26
Pages: 9885-9890
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.0603553103
PMC: PMC1502548
OpenAlex: W2077412035
Cited by: 331
References: 60
Citations by year: 2026: 9 · 2025: 10 · 2024: 13 · 2023: 11 · 2022: 21 · 2021: 15 · 2020: 18 · 2019: 23 · 2018: 24 · 2017: 20 · 2016: 15 · 2015: 23 · 2014: 14 · 2013: 18 · 2012: 19
FWCI: 6.93
Type: journal article
Language: en
Open access: green
europepmc.org
Locations: Proceedings of the National Academy of Sciences (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation (0523908, 0454333, 0349303, 0205671, 0216467)
Alfred P. Sloan Foundation
Division of Chemistry
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
Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
Keywords: protein folding, free energy landscape, reaction coordinate, nonlinear dimensionality reduction, collective coordinates, transition state ensemble, coarse-grained protein models, molecular dynamics sampling
MeSH terms: Algorithms, Models, Molecular, Protein Folding, src Homology Domains, Entropy

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Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

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