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Improved hidden Markov models for molecular motors, Part 2: Extensions and application to experimental data

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Müllner,  Fiona E.
Department: Cellular and Systems Neurobiology / Bonhoeffer, MPI of Neurobiology, Max Planck Society;

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Citation

Syed, S., Müllner, F. E., Selvin, P. R., & Sigworth, F. J. (2010). Improved hidden Markov models for molecular motors, Part 2: Extensions and application to experimental data. Biophysical Journal, 99(11), 3696-3703. doi:10.1016/j.bpj.2010.09.066.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0012-1F3D-A
Abstract
Unbiased interpretation of noisy single molecular motor recordings remains a challenging task To address this issue we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins The basic algorithm called variable stepsize HMM (VS HMM) was introduced in the previous article It improves on currently available Markov model based techniques by allowing for arbitrary distributions of step sizes and shows excellent convergence properties for the characterization of staircase motor timecourses in the presence of large measurement noise In this article we extend the VS HMM framework for better performance with experimental data The extended algorithm variable stepsize integrating detector HMM (VSI HMM) better models the data acquisition process and accounts for random baseline drifts Further as an extension maximum a posteriori estimation is provided When used as a blind step detector the VSI HMM outperforms conventional step detectors The fidelity of the VSI HMM is tested with simulations and is applied to in vitro myosin V data where a small 10 nm population of steps is identified It is also applied to an in vivo recording of melanosome motion where strong evidence is found for repeated bidirectional steps smaller than 8 nm in size implying that multiple motors simultaneously carry the cargo