Literature DB >> 28327454

Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods.

Frank Noé1, Cecilia Clementi2.   

Abstract

Collective variables are an important concept to study high-dimensional dynamical systems, such as molecular dynamics of macromolecules, liquids, or polymers, in particular to define relevant metastable states and state-transition or phase-transition. Over the past decade, a rigorous mathematical theory has been formulated to define optimal collective variables to characterize slow dynamical processes. Here we review recent developments, including a variational principle to find optimal approximations to slow collective variables from simulation data, and algorithms such as the time-lagged independent component analysis. Using these concepts, a distance metric can be defined that quantifies how slowly molecular conformations interconvert. Extensions and open questions are discussed.
Copyright © 2017 Elsevier Ltd. All rights reserved.

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Year:  2017        PMID: 28327454     DOI: 10.1016/j.sbi.2017.02.006

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  19 in total

Review 1.  Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

Authors:  Paraskevi Gkeka; Gabriel Stoltz; Amir Barati Farimani; Zineb Belkacemi; Michele Ceriotti; John D Chodera; Aaron R Dinner; Andrew L Ferguson; Jean-Bernard Maillet; Hervé Minoux; Christine Peter; Fabio Pietrucci; Ana Silveira; Alexandre Tkatchenko; Zofia Trstanova; Rafal Wiewiora; Tony Lelièvre
Journal:  J Chem Theory Comput       Date:  2020-07-16       Impact factor: 6.006

2.  Identification of kinetic order parameters for non-equilibrium dynamics.

Authors:  Fabian Paul; Hao Wu; Maximilian Vossel; Bert L de Groot; Frank Noé
Journal:  J Chem Phys       Date:  2019-04-28       Impact factor: 3.488

3.  Simulations Reveal Multiple Intermediates in the Unzipping Mechanism of Neuronal SNARE Complex.

Authors:  Giovanni Pinamonti; Gregory Campo; Justin Chen; Alex Kluber; Cecilia Clementi
Journal:  Biophys J       Date:  2018-09-07       Impact factor: 4.033

4.  Optimizing model representation for integrative structure determination of macromolecular assemblies.

Authors:  Shruthi Viswanath; Andrej Sali
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-26       Impact factor: 11.205

5.  Size and topology modulate the effects of frustration in protein folding.

Authors:  Alex Kluber; Timothy A Burt; Cecilia Clementi
Journal:  Proc Natl Acad Sci U S A       Date:  2018-08-27       Impact factor: 11.205

6.  Exploring the landscape of model representations.

Authors:  Thomas T Foley; Katherine M Kidder; M Scott Shell; W G Noid
Journal:  Proc Natl Acad Sci U S A       Date:  2020-09-14       Impact factor: 11.205

7.  Modeling the mechanism of CLN025 beta-hairpin formation.

Authors:  Keri A McKiernan; Brooke E Husic; Vijay S Pande
Journal:  J Chem Phys       Date:  2017-09-14       Impact factor: 3.488

8.  Deep learning the slow modes for rare events sampling.

Authors:  Luigi Bonati; GiovanniMaria Piccini; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2021-11-02       Impact factor: 11.205

9.  Comparing the Aggregation Free Energy Landscapes of Amyloid Beta(1-42) and Amyloid Beta(1-40).

Authors:  Weihua Zheng; Min-Yeh Tsai; Peter G Wolynes
Journal:  J Am Chem Soc       Date:  2017-11-07       Impact factor: 15.419

10.  Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol.

Authors:  Ambrose Plante; Harel Weinstein
Journal:  Molecules       Date:  2021-05-20       Impact factor: 4.411

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