TY - GEN A1 - Chodera, John D. A1 - Elms, Philip A1 - Noe, Frank A1 - Keller, Bettina A1 - Kaiser, Christian A1 - Ewall-Wice, Aaron A1 - Marqusee, Susan A1 - Bustamante, Carlos A1 - Singhal Hinrichs, Nina T1 - Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty N2 - Single-molecule force spectroscopy has proven to be a powerful tool for studying the kinetic be- havior 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 in- dividual 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 ob- servations 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 sig- nificant 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 fur- ther 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. Y1 - 2012 UR - https://opus4.kobv.de/opus4-matheon/frontdoor/index/index/docId/1052 UR - https://nbn-resolving.org/urn:nbn:de:0296-matheon-10523 ER -