Hidden-Markov methods for the analysis of single-molecule actomyosin displacement data: The variance-hidden-Markov method

In single-molecule experiments on the interaction between myosin and actin, mechanical events are embedded in Brownian noise. Methods of detecting eve nts have progressed from simple manual detection of shifts in the position record to threshold-based selection of intermittent periods of reduction in noise. However, none of these methods provides a "best fit" to the data. W e have developed a Hidden-Markov algorithm that assumes a simple kinetic mo del for the actin-myosin interaction and provides automatic, threshold-free , maximum-likelihood detection of events. The method is developed for the c ase of a weakly trapped actin-bead dumbbell interacting with a stationary m yosin molecule (Finer, J. T., R. M. Simmons, and J. A. Spudich. 1994. Natur e. 368:113-119). The algorithm operates on the variance of bead position si gnals in a running window, and is tested using Monte Carlo simulations to f ormulate ways of determining the optimum window width. The working stroke i s derived and corrected for actin-bead link compliance. With experimental d ata, we find that modulation of myosin binding by the helical structure of the actin filament complicates the determination of the working stroke; how ever, under conditions that produce a Gaussian distribution of bound levels (cf. Molloy, J. E., J. E. Burns, J. Kendrick-Jones, R. T. Tregear, and D. C. S. White. 1995. Nature. 378:209-212), four experiments gave working stro kes in the range 5.4-6.3 nm for rabbit skeletal muscle myosin S1.