  Single-molecule force spectroscopy has proven to be a powerful tool for
studying the kinetic behavior of biomolecules. Through application of an
external force, conformational states with small or transient populations can
be stabilized, allowing them to be characterized and the statistics of
individual trajectories studied to provide insight into biomolecular folding
and function. Because the observed quantity (force or extension) is not
necessarily an ideal reaction coordinate, individual observations cannot be
uniquely associated with kinetically distinct conformations. While
maximum-likelihood schemes such as hidden Markov models have solved this
problem for other classes of single-molecule experiments by using temporal
information to aid in the inference of a sequence of distinct conformational
states, these methods do not give a clear picture of how precisely the model
parameters are determined by the data due to instrument noise and finite-sample
statistics, both significant problems in force spectroscopy. We solve this
problem through a Bayesian extension that allows the experimental uncertainties
to be directly quantified, and build in detailed balance to further reduce
uncertainty through physical constraints. We illustrate the utility of this
approach in characterizing the three-state kinetic behavior of an RNA hairpin
in a stationary optical trap.
