Abstract
Single-molecule binding assays enable the study of how molecular machines assemble and function. Current algorithms can identify and locate individual molecules, but require tedious manual validation of each spot. Moreover, no solution for high-throughput analysis of single-molecule binding data exists. Here, we describe an automated pipeline to analyze single-molecule data over a wide range of experimental conditions. In addition, our method enables state estimation on multivariate Gaussian signals. We validate our approach using simulated data, and benchmark the pipeline by measuring the binding properties of the well-studied, DNA-guided DNA endonuclease, TtAgo, an Argonaute protein from the Eubacterium Thermus thermophilus. We also use the pipeline to extend our understanding of TtAgo by measuring the protein’s binding kinetics at physiological temperatures and for target DNAs containing multiple, adjacent binding sites.
Highlights
Single-molecule binding assays enable the study of how molecular machines assemble and function
Many key steps for obtaining accurate kinetic parameters from co-localization single-molecule spectroscopy (CoSMoS) images still require manual user intervention and the selection of parameters guided by user experience[7,8,9]
CoSMoS data processing is controlled through a single graphical user-interface, and the modular interface allows individual functional modules to be adjusted for a wide variety of experiments
Summary
Single-molecule binding assays enable the study of how molecular machines assemble and function. 1234567890():,; Single-molecule binding assays allow the interrogation of individual macromolecules from a biological process using purified components or cellular extracts. Many key steps for obtaining accurate kinetic parameters from co-localization single-molecule spectroscopy (CoSMoS) images still require manual user intervention and the selection of parameters guided by user experience[7,8,9]. No standard procedure exists to systematically assess the quality of the analysis To overcome these hurdles, we constructed a pipeline for rapid processing of CoSMoS images while quantitatively assessing experimental data quality. CoSMoS data processing is controlled through a single graphical user-interface, and the modular interface allows individual functional modules to be adjusted for a wide variety of experiments. The pipeline improves detection of co-localization experiments, data analysis speed, and experimental reproducibility
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