Efficient estimation of rare-event kinetics

TL;DR

This paper introduces a general method combining equilibrium distribution knowledge with fast relaxation trajectories to efficiently estimate rare-event kinetics in complex systems, improving sampling efficiency especially for downhill processes.

Contribution

It presents a novel approach that integrates equilibrium data with reversible Markov models to accurately estimate rare-event kinetics without relying on specific dynamical models.

Findings

Significant efficiency gains in sampling rare events.

Effective for downhill processes like folding and binding.

Applicable to complex biomolecular systems.

Abstract

The efficient calculation of rare-event kinetics in complex dynamical systems, such as the rate and pathways of ligand dissociation from a protein, is a generally unsolved problem. Markov state models can systematically integrate ensembles of short simulations and thus effectively parallelize the computational effort, but the rare events of interest still need to be spontaneously sampled in the data. Enhanced sampling approaches, such as parallel tempering or umbrella sampling, can accelerate the computation of equilibrium expectations massively - but sacrifice the ability to compute dynamical expectations. In this work we establish a principle to combine knowledge of the equilibrium distribution with kinetics from fast "downhill" relaxation trajectories using reversible Markov models. This approach is general as it does not invoke any specific dynamical model, and can provide accurate estimates of the rare event kinetics. Large gains in sampling efficiency can be achieved whenever one direction of the process occurs more rapid than its reverse, making the approach especially attractive for downhill processes such as folding and binding in biomolecules.