  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.
