Identification of kinetic order parameters for non-equilibrium dynamics

Abstract

A popular approach to analyze the dynamics of high-dimensional many-body systems, such as macromolecules, is to project the trajectories onto a space of slowly-varying collective variables, where subsequent analyses are made, such as clustering or estimation of free energy profiles or Markov state models (MSMs). However, existing "dynamical" dimension reduction methods, such as the time-lagged independent component analysis (TICA) are only valid if the dynamics obeys detailed balance (microscopic reversibility) and typically require long, equilibrated simulation trajectories. Here we develop a dimension reduction method for non-equilibrium dynamics based on the recently developed Variational Approach for Markov Processes (VAMP) by Wu and Noé. VAMP is illustrated by obtaining a low-dimensional description of a single file ion diffusion model and by identifying long-lived states from molecular dynamics simulations of the KcsA channel protein in an external electrochemical potential. This analysis provides detailed insights into the coupling of conformational dynamics, the configuration of the selectivity filter, and the conductance of the channel. We recommend VAMP as a replacement for the less general TICA method.

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Cited by in corpus (9)

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• Special Topic: Markov Models of Molecular Kinetics
• Variational Selection of Features for Molecular Kinetics
• Variational approach for learning Markov processes from time series data
• A Deep Autoencoder Framework for Discovery of Metastable Ensembles in Biomacromolecules
• Towards a Benchmark for Markov State Models: The Folding of HP35
• Deflation reveals dynamical structure in nondominant reaction coordinates
• Data-driven Langevin modeling of nonequilibrium processes
• Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model "microstates" using non-Markovian theory