Perspective: Markov Models for Long-Timescale Biomolecular Dynamics

TL;DR

This paper discusses the use of Markov models for analyzing large-scale biomolecular simulations, highlighting recent improvements, open issues, and theoretical advances to better understand molecular kinetics.

Contribution

It reviews recent developments and open challenges in applying Markov models to biomolecular simulation data, proposing a new direction for molecular kinetics modeling.

Findings

Recent improvements in Markov model construction

Identification of open issues in model application

Theoretical advances enabling next-generation models

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.