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. When used as a blind step detector, the VSI-HMM outperforms conventional step detectors. Further, as an extension, maximum a posteriori estimation is provided. The extended algorithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and accounts for random baseline drifts. In this article, we extend the VS-HMM framework for better performance with experimental data. 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.
The basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article. To address this issue, we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins. Unbiased interpretation of noisy single molecular motor recordings remains a challenging task.
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