This Article Full Text Full Text (PDF) All Versions of this Article: biophysj.105.079541v1 91/4/1156    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Milescu, L. S. Articles by Sachs, F. Search for Related Content PubMed PubMed Citation Articles by Milescu, L. S. Articles by Sachs, F.

Maximum Likelihood Estimation of Molecular Motor Kinetics from Staircase Dwell-Time Sequences

Lorin S. Milescu ^*, Ahmet Yildiz , Paul R. Selvin   and Frederick Sachs ^*

^* Department of Physiology and Biophysics, State University of New York at Buffalo, Buffalo, New York; and Physics Department and Biophysics Center, University of Illinois at Urbana-Champaign, Urbana, Illinois

Correspondence: Address reprint requests to F. Sachs, Dept. of Physiology and Biophysics, State University of New York at Buffalo, Buffalo, NY. E-mail: sachs{at}buffalo.edu.

Molecular motors, such as kinesin, myosin, or dynein, convert chemical energy into mechanical energy by hydrolyzing ATP. The mechanical energy is used for moving in discrete steps along the cytoskeleton and carrying a molecular load. High resolution single molecule recordings of motor steps appear as a stochastic sequence of dwells, resembling a staircase. Staircase data can also be obtained from other molecular machines such as F₁-ATPase, RNA polymerase, or topoisomerase. We developed a maximum likelihood algorithm that estimates the rate constants between different conformational states of the protein, including motor steps. We model the motor with a periodic Markov model that reflects the repetitive chemistry of the motor step. We estimated the kinetics from the idealized dwell-sequence by numerical maximization of the likelihood function for discrete-time Markov models. This approach eliminates the need for missed event correction. The algorithm can fit kinetic models of arbitrary complexity, such as uniform or alternating step chemistry, reversible or irreversible kinetics, ATP concentration and mechanical force-dependent rates, etc. The method allows global fitting across stationary and nonstationary experimental conditions, and user-defined a priori constraints on rate constants. The algorithm was tested with simulated data, and implemented in the free QuB software.

This article has been cited by other articles:

				B. C. Carter, M. Vershinin, and S. P. Gross A Comparison of Step-Detection Methods: How Well Can You Do? Biophys. J., January 1, 2008; 94(1): 306 - 319. [Abstract] [Full Text] [PDF]

				M. Linden and M. Wallin Dwell Time Symmetry in Random Walks and Molecular Motors Biophys. J., June 1, 2007; 92(11): 3804 - 3816. [Abstract] [Full Text] [PDF]

				L. S. Milescu, A. Yildiz, P. R. Selvin, and F. Sachs Extracting Dwell Time Sequences from Processive Molecular Motor Data Biophys. J., November 1, 2006; 91(9): 3135 - 3150. [Abstract] [Full Text] [PDF]