Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses

Ernesto Suárez, Adam J. Pratt, Lillian T. Chong, Daniel Zuckerman

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

First-passage times (FPTs) are widely used to characterize stochastic processes such as chemical reactions, protein folding, diffusion processes or triggering a stock option. In previous work (Suarez et al., JCTC 2014;10:2658-2667), we demonstrated a non-Markovian analysis approach that, with a sufficient subset of history information, yields unbiased mean first-passage times from weighted-ensemble (WE) simulations. The estimation of the distribution of the first-passage times is, however, a more ambitious goal since it cannot be obtained by direct observation in WE trajectories. Likewise, a large number of events would be required to make a good estimation of the distribution from a regular "brute force" simulation. Here, we show how the previously developed non-Markovian analysis can generate approximate, but highly accurate, FPT distributions from WE data. The analysis can also be applied to any other unbiased trajectories, such as from standard molecular dynamics simulations. The present study employs a range of systems with independent verification of the distributions to demonstrate the success and limitations of the approach. By comparison to a standard Markov analysis, the non-Markovian approach is less sensitive to the user-defined discretization of configuration space.

Original languageEnglish (US)
Pages (from-to)67-78
Number of pages12
JournalProtein Science
Volume25
Issue number1
DOIs
Publication statusPublished - Jan 1 2016
Externally publishedYes

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Keywords

  • first-passage time
  • non-Markovian
  • rare event
  • weighted ensemble

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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