Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty
Please always quote using this URN:urn:nbn:de:0296-matheon-10523
- 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.
Author: | John D. Chodera, Philip Elms, Frank Noe, Bettina Keller, Christian Kaiser, Aaron Ewall-Wice, Susan Marqusee, Carlos Bustamante, Nina Singhal Hinrichs |
---|---|
URN: | urn:nbn:de:0296-matheon-10523 |
Referee: | Christof Schütte |
Document Type: | Preprint, Research Center Matheon |
Language: | English |
Date of first Publication: | 2012/02/02 |
Release Date: | 2012/02/02 |
Institute: | Research Center Matheon |
Freie Universität Berlin | |
MSC-Classification: | 60-XX PROBABILITY THEORY AND STOCHASTIC PROCESSES (For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX) / 60Jxx Markov processes / 60J05 Discrete-time Markov processes on general state spaces |
Preprint Number: | 913 |