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

2011/08/06 by John D. Chodera, Chodera, John D., Phillip Elms +19 · 21 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Atomic force microscopy #Bayesian probability #Biological Physics (physics.bio-ph) #Biological system #Biomolecules (q-bio.BM) #Classical mechanics #Computer science #Econometrics #Electrochemical Analysis and Applications #FOS: Biological sciences #FOS: Physical sciences #Force Microscopy Techniques and Applications #Force spectroscopy #Hidden Markov model #Kinetics #Machine Learning in Materials Science #Machine learning #Markov chain #Markov model #Materials science #Mathematics #Nanotechnology #Physics #Statistical Mechanics (cond-mat.stat-mech) #Statistical physics #cond-mat.stat-mech #physics.bio-ph #q-bio.BM

paper · pdf · doi:10.48550/arxiv.1108.1430

published in arXiv (Cornell University) (Cornell University)

arxiv created 2011/08/06 · openalex publication_date 2011/08/06 · arxiv updated 2015/03/12 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

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.

Citations

• Markov models of molecular kinetics: Generation and validation
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• A tutorial on hidden Markov models and selected applications in speech recognition
• Monte Carlo sampling methods using Markov chains and their applications

Cited by

• A cautious user's guide in applying HMMs to physical systems

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