The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
Determination of reaction coordinates via locally scaled diffusion map
Abstract
We present a multiscale method for the determination of collective reaction coordinates for macromolecular dynamics based on two recently developed mathematical techniques: diffusion map and the determination of local intrinsic dimensionality of large datasets. Our method accounts for the local variation of molecular configuration space, and the resulting global coordinates are correlated with the time scales of the molecular motion. To illustrate the approach, we present results for two model systems: all-atom alanine dipeptide and coarse-grained src homology 3 protein domain. We provide clear physical interpretation for the emerging coordinates and use them to calculate transition rates. The technique is general enough to be applied to any system for which a Boltzmann-sampled set of molecular configurations is available.
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In the citing paper
“the construction of diffusion maps—which aim to approximate these eigenfunctions for overdamped Langevin dynamics—have been extended to model biomolecular dynamics, despite being originally introduced as a general data analysis tool [10].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [10]
In the citing paper
“methods such as diffusion maps 9,77-79 try to link geometric data to the characteristic time scale of the system's motion.”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [79]
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Cites
GROMACS: Fast, flexible, and free
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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
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
Brownian motion in a field of force and the diffusion model of chemical reactions
Kramers, H. A. (1940) · Physica · doi:10.1016/s0031-8914(40)90098-2 · Cited by 8,561
Dunteman, G. H. (1989) · Sage eBooks · doi:10.4135/9781412985475 · Cited by 2,713
Coifman, R. R., & Lafon, S. (2006) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2006.04.006 · Cited by 2,380
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675
Self-citationClementi, C., Nymeyer, H., & Onuchic, J. N. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3693 · Cited by 1,228
Self-citationTransition path sampling and the calculation of rate constants
Dellago, C., Bolhuis, P. G., Csajka, F. S., & Chandler, D. (1998) · The Journal of Chemical Physics · doi:10.1063/1.475562 · Cited by 1,072
E, W., Ren, W.-Q., & Vanden-Eijnden, E. (2007) · Journal of Chemical Physics · doi:10.1063/1.2720838 · Cited by 728
Research works printed but not linked: 1
- 2007, J. Mach. Learn. Res., 8, 2169
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Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
Scherer, M. K., Trendelkamp-Schroer, B., Paul, F., Pérez‐Hernández, G., Hoffmann, M., Plattner, N., Wehmeyer, C., Prinz, J.-H., & Noé, F. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00743 · Cited by 1,160
Identification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundMarkov state models of biomolecular conformational dynamics
Chodera, J. D., & Noé, F. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.04.002 · Cited by 776
Using metadynamics to explore complex free-energy landscapes
Bussi, G., & Laio, A. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-0153-0 · Cited by 726
Metadata
- Authors
- Rohrdanz, M. A., Zheng, W., Maggioni, M., & Clementi, C.
- Affiliations
- Rice University (US)
- Duke University (US)
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2011-03-28
- Volume
- 134
- Issue
- 12
- Pages
- 124116-124116
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W1971725855
- Cited by
- 292
- References
- 40
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 15 · 2023: 12 · 2022: 14 · 2021: 18 · 2020: 18 · 2019: 22 · 2018: 23 · 2017: 46 · 2016: 24 · 2015: 25 · 2014: 19 · 2013: 19 · 2012: 9
- FWCI
- 7.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0835712, 0835824, CHE-0349303, CNS-0421109, CNS-0821727, DMS-0650413, EIA-0216467)
- 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 › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- reaction coordinate, diffusion maps, collective variables, macromolecular dynamics, intrinsic dimension, multiscale methods, alanine dipeptide, SH3 domain, transition rates
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