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Single-molecule force spectroscopy has proven to be a powerful tool forstudying the kinetic behavior of biomolecules. Through application of anexternal force, conformational states with small or transient populations canbe stabilized, allowing them to be characterized and the statistics ofindividual trajectories studied to provide insight into biomolecular foldingand function. Because the observed quantity (force or extension) is notnecessarily an ideal reaction coordinate, individual observations cannot beuniquely associated with kinetically distinct conformations. Whilemaximum-likelihood schemes such as hidden Markov models have solved thisproblem for other classes of single-molecule experiments by using temporalinformation to aid in the inference of a sequence of distinct conformationalstates, these methods do not give a clear picture of how precisely the modelparameters are determined by the data due to instrument noise and finite-samplestatistics, both significant problems in force spectroscopy. We solve thisproblem through a Bayesian extension that allows the experimental uncertaintiesto be directly quantified, and build in detailed balance to further reduceuncertainty through physical constraints. We illustrate the utility of thisapproach in characterizing the three-state kinetic behavior of an RNA hairpinin a stationary optical trap.

Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty