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要旨:
Cytoskeletal motor proteins are essential to the function of a wide range of intracellular mechano-systems. The biophysical characterization of their movement along their filamentous tracks is therefore of large importance. Toward this end, single-molecule, in vitro stepping-motility assays are commonly used to determine motor velocity and run length. However, comparing results from such experiments has proved difficult due to influences from variations in the experimental conditions and the data analysis methods. Here, we investigate the movement of fluorescently labeled, processive, dimeric motor proteins and propose a unified algorithm to correct the measurements for finite filament length as well as photobleaching. Particular emphasis is put on estimating the statistical errors associated with the proposed evaluation method, as knowledge of these values is crucial when comparing measurements from different experiments. Testing our approach with simulated and experimental data from GFP-labeled kinesin-1 motors stepping along immobilized microtubules, we show 1) that velocity distributions should be fitted by a t location-scale probability density function rather than by a normal distribution; 2) that the impossibility to measure events shorter than the image acquisition time needs to be taken into account; 3) that the interaction time and run length of the motors can be estimated independent of the filament length distribution; and 4) that the dimeric nature of the motors needs to be considered when correcting for photobleaching. Moreover, our analysis reveals that controlling the temperature during the experiments with a precision below 1 K is of importance. We believe our method will not only improve the evaluation of experimental data, but also allow for better statistical comparisons between different populations of motor proteins (e.g., with distinct mutations or linked to different cargos) and filaments (e.g., in distinct nucleotide states or with different posttranslational modifications). Therefore, we include a detailed workflow for image processing and analysis (including MATLAB code), serving as a tutorial for the estimation of motility parameters in stepping-motility assays.
