Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model 'microstates' using non-Markovian theory

التفاصيل البيبلوغرافية
العنوان: Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model 'microstates' using non-Markovian theory
المؤلفون: Copperman, Jeremy, Zuckerman, Daniel
سنة النشر: 2019
المجموعة: Condensed Matter
Physics (Other)
مصطلحات موضوعية: Condensed Matter - Statistical Mechanics, Physics - Biological Physics, Physics - Chemical Physics
الوصف: The weighted ensemble (WE) simulation strategy provides unbiased sampling of non-equilibrium processes, such as molecular folding or binding, but the extraction of rate constants relies on characterizing steady state behavior. Unfortunately, WE simulations of sufficiently complex systems will not relax to steady state on observed simulation times. Here we show that a post-simulation clustering of molecular configurations into "microbins" using methods developed in the Markov State Model (MSM) community, can yield unbiased kinetics from WE data before steady-state convergence of the WE simulation itself. Because WE trajectories are directional and not equilibrium-distributed, the history-augmented MSM (haMSM) formulation can be used, which yields the mean first-passage time (MFPT) without bias for arbitrarily small lag times. Accurate kinetics can be obtained 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 then analyze 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.
نوع الوثيقة: Working Paper
DOI: 10.1016/j.bpj.2019.11.1099
URL الوصول: http://arxiv.org/abs/1903.04673
رقم الانضمام: edsarx.1903.04673
قاعدة البيانات: arXiv
الوصف
DOI:10.1016/j.bpj.2019.11.1099