RNA Therapeutics Institute; Graduate School of Biomedical Sciences
Amino Acids, Peptides, and Proteins | Biochemistry | Biophysics | Genetics and Genomics | Laboratory and Basic Science Research | Molecular Biology | Structural Biology
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.
Biophysical chemistry, Single-molecule biophysics
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Copyright © The Author(s) 2019. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
DOI of Published Version
Nat Commun. 2019 Jan 17;10(1):272. doi: 10.1038/s41467-018-08045-5. Link to article on publisher's site
Smith, Carlas; Jouravleva, Karina; Huisman, Maximiliaan; Jolly, Samson M.; Zamore, Phillip D.; and Grünwald, David, "An automated Bayesian pipeline for rapid analysis of single-molecule binding data" (2019). Open Access Articles. 3712.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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