The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 [95]
Citations
Cites
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
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
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
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
Research works printed but not linked: 7
- 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).
Cited by
Sorted by citations
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 656
Schwantes, C. R., & Pande, V. S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300878a · Cited by 597
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Cśanyi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 594
Abrams, C. F., & Bussi, G. (2013) · Entropy · doi:10.3390/e16010163 · Cited by 441
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A., Ortner, C., Cśanyi, G., Ceriotti, M., et al. (2021) · Cambridge University Engineering Department Publications Database · Cited by 440
Metadata
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
