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Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations

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Stelzl,  Lukas S.
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;

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Hummer,  Gerhard       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany;

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Citation

Stelzl, L. S., Kells, A., Rosta, E., & Hummer, G. (2017). Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations. Journal of Chemical Theory and Computation, 13(12), 6328-6342. doi:10.1021/acs.jctc.7b00373.


Cite as: https://hdl.handle.net/21.11116/0000-0001-29CB-D
Abstract
We present an algorithm to calculate free energies and rates from molecular simulations on biased potential energy surfaces. As input, it uses the accumulated times spent in each state or bin of a histogram and counts of transitions between them. Optimal unbiased equilibrium free energies for each of the states/bins are then obtained by maximizing the likelihood of a master equation (i.e., first-order kinetic rate model). The resulting free energies also determine the optimal rate coefficients for transitions between the states or bins on the biased potentials. Unbiased rates can be estimated, e.g., by imposing a linear free energy condition in the likelihood maximization. The resulting “dynamic histogram analysis method extended to detailed balance” (DHAMed) builds on the DHAM method. It is also closely related to the transition-based reweighting analysis method (TRAM) and the discrete TRAM (dTRAM). However, in the continuous-time formulation of DHAMed, the detailed balance constraints are more easily accounted for, resulting in compact expressions amenable to efficient numerical treatment. DHAMed produces accurate free energies in cases where the common weighted-histogram analysis method (WHAM) for umbrella sampling fails because of slow dynamics within the windows. Even in the limit of completely uncorrelated data, where WHAM is optimal in the maximum-likelihood sense, DHAMed results are nearly indistinguishable. We illustrate DHAMed with applications to ion channel conduction, RNA duplex formation, α-helix folding, and rate calculations from accelerated molecular dynamics. DHAMed can also be used to construct Markov state models from biased or replica-exchange molecular dynamics simulations. By using binless WHAM formulated as a numerical minimization problem, the bias factors for the individual states can be determined efficiently in a preprocessing step and, if needed, optimized globally afterward.