Determination of reaction coordinates via locally scaled diffusion map

Rohrdanz, Mary A.; Zheng, Wenwei; Maggioni, Mauro; Clementi, Cecilia

doi:10.1063/1.3569857

Cited by 292Protein Structure and DynamicsAdvanced Physical and Chemical Molecular InteractionsModel Reduction and Neural Networks

Determination of reaction coordinates via locally scaled diffusion map

Rohrdanz, Mary A., Zheng, Wenwei, Maggioni, Mauro, Clementi, Cecilia·2011·The Journal of Chemical Physics

DOI 10.1063/1.3569857

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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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The construction of diffusion maps has been extended to model biomolecular dynamics.
“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]
Diffusion maps try to link geometric data to the characteristic time scale of the system's motion.
“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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Authors: Rohrdanz, M. A., Zheng, W., Maggioni, M., & Clementi, C.
Affiliations: Rice University (US)
Duke University (US)
ORCID: Zheng, Wenwei
Maggioni, Mauro
Clementi, Cecilia
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
DOI: 10.1063/1.3569857
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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Determination of reaction coordinates via locally scaled diffusion map

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