Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models

Ernesto Suárez, Joshua L. Adelman, Daniel Zuckerman

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Because standard molecular dynamics (MD) simulations are unable to access time scales of interest in complex biomolecular systems, it is common to "stitch together" information from multiple shorter trajectories using approximate Markov state model (MSM) analysis. However, MSMs may require significant tuning and can yield biased results. Here, by analyzing some of the longest protein MD data sets available (>100 μs per protein), we show that estimators constructed based on exact non-Markovian (NM) principles can yield significantly improved mean first-passage times (MFPTs) for protein folding and unfolding. In some cases, MSM bias of more than an order of magnitude can be corrected when identical trajectory data are reanalyzed by non-Markovian approaches. The NM analysis includes "history" information, higher order time correlations compared to MSMs, that is available in every MD trajectory. The NM strategy is insensitive to fine details of the states used and works well when a fine time-discretization (i.e., small "lag time") is used.

Original languageEnglish (US)
Pages (from-to)3473-3481
Number of pages9
JournalJournal of Chemical Theory and Computation
Volume12
Issue number8
DOIs
StatePublished - Aug 9 2016
Externally publishedYes

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Protein folding
folding
MSM (semiconductors)
Molecular dynamics
Trajectories
trajectories
molecular dynamics
proteins
access time
Proteins
complex systems
estimators
Large scale systems
time lag
Tuning
tuning
histories
Computer simulation
simulation

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

Cite this

Accurate Estimation of Protein Folding and Unfolding Times : Beyond Markov State Models. / Suárez, Ernesto; Adelman, Joshua L.; Zuckerman, Daniel.

In: Journal of Chemical Theory and Computation, Vol. 12, No. 8, 09.08.2016, p. 3473-3481.

Research output: Contribution to journalArticle

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