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Kinetics from Metadynamics: Principles, Applications, and Outlook

Cite this: J. Chem. Theory Comput. 2023, 19, 17, 5649–5670
Publication Date (Web):August 16, 2023
https://doi.org/10.1021/acs.jctc.3c00660
Copyright © 2023 American Chemical Society

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    Abstract

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    Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein–ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.

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    Cited By

    This article is cited by 6 publications.

    1. Jintu Zhang, Odin Zhang, Luigi Bonati, TingJun Hou. Combining Transition Path Sampling with Data-Driven Collective Variables through a Reactivity-Biased Shooting Algorithm. Journal of Chemical Theory and Computation 2024, Article ASAP.
    2. Subarna Sasmal, Triasha Pal, Glen M. Hocky, Martin McCullagh. Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM. Journal of Chemical Theory and Computation 2024, 20 (9) , 3492-3502. https://doi.org/10.1021/acs.jctc.4c00223
    3. Ofir Blumer, Shlomi Reuveni, Barak Hirshberg. Short-Time Infrequent Metadynamics for Improved Kinetics Inference. Journal of Chemical Theory and Computation 2024, 20 (9) , 3484-3491. https://doi.org/10.1021/acs.jctc.4c00170
    4. Dhiman Ray, Sudip Das, Umberto Raucci. Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations. Journal of Chemical Information and Modeling 2024, 64 (9) , 3953-3958. https://doi.org/10.1021/acs.jcim.4c00475
    5. Haohao Fu, Hengwei Bian, Xueguang Shao, Wensheng Cai. Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning. The Journal of Physical Chemistry Letters 2024, 15 (6) , 1774-1783. https://doi.org/10.1021/acs.jpclett.3c03542
    6. Haohao Fu, Christophe Chipot, Xueguang Shao, Wensheng Cai. Standard Binding Free-Energy Calculations: How Far Are We from Automation?. The Journal of Physical Chemistry B 2023, 127 (49) , 10459-10468. https://doi.org/10.1021/acs.jpcb.3c04370

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