%0 Generic %1 Copperman2019haMSMWE %A Copperman, Jeremy %A Zuckerman, Daniel %D 2019 %K enhanced-sampling path-sampling weighted-ensemble %R 10.1016/j.bpj.2019.11.1099 %T Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model "microstates" using non-Markovian theory %U http://arxiv.org/abs/1903.04673 %X The weighted ensemble (WE) simulation strategy can provide unbiased sampling of non-equilibrium processes, such as molecular folding or binding. Unbiased kinetic rates can be extracted from any discrete clustering of the configuration space based on a history-augmented Markov state model (haMSM) at any lag time, in the steady-state. However, the convergence of WE to steady-state may require unaffordably long simulations in complex systems. Here we show that by clustering molecular configurations into many (thousands of) microbins using methods developed in the Markov State Modeling (MSM) community, unbiased kinetics can be obtained from WE data using history-augmented Markov State Models (haMSMs) before steady-state convergence of the WE simulation itself. Because arbitrarily small lag times can be used within the exact haMSM formulation, accurate kinetics can be obtained with significantly less trajectory data than traditional MSMs, while bypassing the often prohibitive convergence requirements of the non-equilibrium weighted ensemble. We validate the method in a simple diffusive process on a 2D random energy landscape, and apply the method to atomistic protein folding simulations using WE molecular dynamics. We report significant progress towards the unbiased estimation of protein folding times and pathways, though key challenges remain.

@misc{Copperman2019haMSMWE, abstract = {The weighted ensemble (WE) simulation strategy can provide unbiased sampling of non-equilibrium processes, such as molecular folding or binding. Unbiased kinetic rates can be extracted from any discrete clustering of the configuration space based on a history-augmented Markov state model (haMSM) at any lag time, in the steady-state. However, the convergence of WE to steady-state may require unaffordably long simulations in complex systems. Here we show that by clustering molecular configurations into many (thousands of) microbins using methods developed in the Markov State Modeling (MSM) community, unbiased kinetics can be obtained from WE data using history-augmented Markov State Models (haMSMs) before steady-state convergence of the WE simulation itself. Because arbitrarily small lag times can be used within the exact haMSM formulation, accurate kinetics can be obtained with significantly less trajectory data than traditional MSMs, while bypassing the often prohibitive convergence requirements of the non-equilibrium weighted ensemble. We validate the method in a simple diffusive process on a 2D random energy landscape, and apply the method to atomistic protein folding simulations using WE molecular dynamics. We report significant progress towards the unbiased estimation of protein folding times and pathways, though key challenges remain.}, added-at = {2020-07-10T22:17:52.000+0200}, author = {Copperman, Jeremy and Zuckerman, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/256aa573457853e3c84ffe1d2cb1b7fe7/salotz}, description = {[1903.04673] Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model "microstates" using non-Markovian theory}, doi = {10.1016/j.bpj.2019.11.1099}, interhash = {a7ab84031d66d2c02c8ae7043444bedd}, intrahash = {56aa573457853e3c84ffe1d2cb1b7fe7}, keywords = {enhanced-sampling path-sampling weighted-ensemble}, note = {cite arxiv:1903.04673}, timestamp = {2020-07-10T22:17:52.000+0200}, title = {Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model "microstates" using non-Markovian theory}, url = {http://arxiv.org/abs/1903.04673}, year = 2019 }