Multiensemble Markov models of molecular thermodynamics and kinetics

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 113; no. 23; pp. E3221 - E3230
• Main Authors: Wu, Hao, Paul, Fabian, Wehmeyer, Christoph, Noé, Frank
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 07.06.2016
• Series: PNAS Plus
• Subjects: Biological Sciences; Kinetics; Markov analysis; Physical Sciences; PNAS Plus; Sampling; Simulation; Thermodynamics; Markov state models; transition-based reweighting analysis method; molecular dynamics; enhanced sampling; Bennett acceptance ratio
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1525092113

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov statemodels—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model.