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 Markovmodel 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3696-3703 |
| Number of pages | 8 |
| Journal | Biophysical Journal |
| Volume | 99 |
| Issue number | 11 |
| DOIs | |
| State | Published - Dec 1 2010 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Biophysics
Cite this
Improved hidden Markov models for molecular motors, part 2 : Extensions and application to experimental data. / Syed, Sheyum; Müllner, Fiona E.; Selvin, Paul R.; Sigworth, Fred J.
In: Biophysical Journal, Vol. 99, No. 11, 01.12.2010, p. 3696-3703.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improved hidden Markov models for molecular motors, part 2
T2 - Extensions and application to experimental data
AU - Syed, Sheyum
AU - Müllner, Fiona E.
AU - Selvin, Paul R.
AU - Sigworth, Fred J.
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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 Markovmodel 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.
AB - 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 Markovmodel 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.
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U2 - 10.1016/j.bpj.2010.09.066
DO - 10.1016/j.bpj.2010.09.066
M3 - Article
C2 - 21112294
AN - SCOPUS:78649818686
VL - 99
SP - 3696
EP - 3703
JO - Biophysical Journal
JF - Biophysical Journal
SN - 0006-3495
IS - 11
ER -