In addition to breakthroughs in computer hardware ( 16), simulation software ( 17– 19), and distributed computing ( 20, 21), a key technology to reconcile swarms of individually short simulations to long-time kinetics are kinetic models, such as Markov state models (MSMs) ( 22– 28).
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Unbiased atomistic molecular dynamics (MD) simulations have recently reached the ability to extensively sample biomolecular processes on timescales up to milliseconds, including protein folding ( 11), conformational changes ( 5, 12), and protein–ligand association and dissociation ( 13– 15).
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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.Ĭomputer simulations have become important tools in the investigation of biomolecular processes, including transmembrane transport ( 1– 4), ligand reception and receptor activation ( 5– 7), and endocytosis ( 8– 10).
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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 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. The approach combines the benefits of Markov state models-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 estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. 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.