We propose a discrete transition-based reweighting analysis method (TRAM) for analyzing molecular simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) TRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to traditional estimators such as WHAM and MBAR, TRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, TRAM provides optimal estimates of Markov state models (MSMs) at all thermodynamic states simulated at. Under suitable conditions, these MSMs can be used to calculate kinetic quantities (e.g. rates, timescales). In the limit of a single thermodynamic state, TRAM estimates a maximum likelihood reversible MSM, while in the limit of uncorrelated sampling data, TRAM is identical to MBAR or WHAM (when binning is used). dTRAM is thus a generalization to both estimators. I will demonstrate the versatility of TRAM using atomistic simulations of protein-ligand binding processes.

TRAM: Optimal estimation of trajectory data from multiple thermodynamic states

249th National Meeting (2015)

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