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

11 Citations (Scopus)

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
StatePublished - Jan 1 2016
Externally publishedYes

Fingerprint

Trajectories
Protein folding
Random processes
Molecular dynamics
Chemical reactions
Stochastic Processes
Protein Folding
Computer simulation
Molecular Dynamics Simulation
History
Observation

Keywords

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

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

Cite this

Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses. / Suárez, Ernesto; Pratt, Adam J.; Chong, Lillian T.; Zuckerman, Daniel.

In: Protein Science, Vol. 25, No. 1, 01.01.2016, p. 67-78.

Research output: Contribution to journalArticle

Suárez, Ernesto ; Pratt, Adam J. ; Chong, Lillian T. ; Zuckerman, Daniel. / Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses. In: Protein Science. 2016 ; Vol. 25, No. 1. pp. 67-78.
@article{a1b298107ba949639ea64b75079464c8,
title = "Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses",
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.",
keywords = "first-passage time, non-Markovian, rare event, weighted ensemble",
author = "Ernesto Su{\'a}rez and Pratt, {Adam J.} and Chong, {Lillian T.} and Daniel Zuckerman",
year = "2016",
month = "1",
day = "1",
doi = "10.1002/pro.2738",
language = "English (US)",
volume = "25",
pages = "67--78",
journal = "Protein Science",
issn = "0961-8368",
publisher = "Cold Spring Harbor Laboratory Press",
number = "1",

}

TY - JOUR

T1 - Estimating first-passage time distributions from weighted ensemble simulations and non-Markovian analyses

AU - Suárez, Ernesto

AU - Pratt, Adam J.

AU - Chong, Lillian T.

AU - Zuckerman, Daniel

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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.

AB - 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.

KW - first-passage time

KW - non-Markovian

KW - rare event

KW - weighted ensemble

UR - http://www.scopus.com/inward/record.url?scp=84959230759&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959230759&partnerID=8YFLogxK

U2 - 10.1002/pro.2738

DO - 10.1002/pro.2738

M3 - Article

VL - 25

SP - 67

EP - 78

JO - Protein Science

JF - Protein Science

SN - 0961-8368

IS - 1

ER -