title: 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.
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California 94305, USA.
authors.forename: C R
authors.initials: CR
authors.lastname: Schwantes
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California 94305, USA.
authors.forename: R T
authors.initials: RT
authors.lastname: McGibbon
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California 94305, USA.
authors.forename: V S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D008390:Markov Chains; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D011943:Receptors, Adrenergic, beta / Q000737:chemistry; D001618:beta-Lactamases / Q000737:chemistry
chemical_list: D011506:Proteins; D011943:Receptors, Adrenergic, beta; D001618:beta-Lactamases