Perspective: Markov Models for Long-Timescale Biomolecular Dynamics

Abstract

Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.

7 pages

References in corpus (5)

• Identification of slow molecular order parameters for Markov model construction
• Projected and Hidden Markov Models for calculating kinetics and metastable states of complex molecules
• Small-world networks and the conformation space of a lattice polymer chain
• Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty
• Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

Cited by in corpus (11)

• Galerkin Approximation of Dynamical Quantities using Trajectory Data
• Relaxation Mode Analysis and Markov State Relaxation Mode Analysis for Chignolin in Aqueous Solution near a Transition Temperature
• Integrated VAC: A robust strategy for identifying eigenfunctions of dynamical operators
• Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
• Optimization of Non-Equilibrium Self-Assembly Protocols Using Markov State Models
• Perspective: Time irreversibility in systems observed at coarse resolution
• Modeling of biomolecular machines in non-equilibrium steady states
• Dynamic coarse-graining fills the gap between atomistic simulations and experimental investigations of mechanical unfolding
• Markov State Modeling of Sliding Friction
• A Markov State Modeling analysis of sliding dynamics of a 2D model
• Simulating cw-ESR Spectrum Using Discrete Markov Model of Single Brownian Trajectory