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Area of Science:

  • Computational Chemistry and Molecular Dynamics
  • Biophysics and Statistical Mechanics

Background:

  • Rare-event processes in molecular systems (e.g., protein folding) are crucial but often exceed standard simulation timescales.
  • Weighted ensemble (WE) simulations accelerate sampling but their efficiency depends heavily on trajectory resampling strategies.

Purpose of the Study:

  • To introduce CoWERA (Coherence-based Weighted Ensemble Resampling Algorithm), a novel WE resampling strategy.
  • To enhance the efficiency and robustness of WE simulations for rare molecular events.

Main Methods:

  • Developed CoWERA, a binless, targeted WE resampling strategy utilizing 'temporal coherence'.
  • Defined 'trajectory intensity' based on signed progress over an adaptive history window.
  • Benchmarked CoWERA on chignolin and Trp-cage miniproteins against conventional and proximity-based WE methods.

Main Results:

  • CoWERA demonstrated rapid generation of reactive events and faster stabilization of rate estimates.
  • Showed reduced run-to-run variability compared to existing WE methods.
  • Reproduces reference folding/unfolding kinetics for Trp-cage with significantly reduced simulation time.

Conclusions:

  • Incorporating history-dependent temporal coherence into resampling decisions enhances WE simulation efficiency and robustness.
  • CoWERA offers a more effective approach for estimating rare event kinetics in molecular systems.