Abstract
SummarySingle-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.
Highlights
These techniques have made it possible to track the molecular dynamics of individual proteins and protein complexes with ananometer spatial resolution and amillisecond timescale.[3,4]
AutoStepfinder runs a first round of step fitting, in which the algorithm minimizes the variance (s2) between the data and the fit
AutoStepfinder determines the quality of the fit by performing an additional fit[43,51,52] (Figures 1B and S1)
Summary
Over the last 25 years, single-molecule techniques have greatly enhanced our understanding of complex biological processes.[1,2] These techniques have made it possible to track the molecular dynamics of individual proteins and protein complexes with a (sub)nanometer spatial resolution and a (sub)millisecond timescale.[3,4] For example, molecular motor protein complexes were observed to move in a step-by-step fashion along cytoskeleton filaments.[5,6,7] More generally, force spectroscopy (using, e.g., optical or magnetic tweezers) has been exploited as a versatile tool for probing the forces and motions that are associated with biological macromolecules.[8,9] Singlemolecule fluorescence techniques have been used to determine the stoichiometry, binding kinetics, and conformational dynamics of nucleic acids and proteins.[10,11,12,13] Nanopores have provided a powerful tool for the label-free detection of nucleic acids and proteins.[14,15]. Principles of step detection The AutoStepfinder algorithm fits data through a series of partition events that minimize s2 (Figure 1). When the steps in the data are within the same order of magnitude (e.g., D1 or D2 in Figures 4A and 4B), the Stepfinder algorithm would plot the S-curve and require the user to select the optimal fit by providing the number of iterations that corresponds to the global maximum of the S-curve (Smax) (Figure 4D).
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