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

TL;DR

This paper introduces a Bayesian hidden Markov model to analyze single-molecule force spectroscopy data, effectively quantifying uncertainties and incorporating physical constraints to better characterize biomolecular kinetics.

Contribution

The authors develop a Bayesian hidden Markov model that accounts for measurement noise and physical constraints, improving kinetic analysis in force spectroscopy.

Findings

Successfully characterized three-state RNA hairpin kinetics

Quantified uncertainties in model parameters

Enhanced inference accuracy with physical constraints

Abstract

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.