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Multi-parameter photon-by-photon hidden Markov modeling

Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H(2)MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on th...

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Autores principales: Harris, Paul David, Narducci, Alessandra, Gebhardt, Christian, Cordes, Thorben, Weiss, Shimon, Lerner, Eitan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863987/
https://www.ncbi.nlm.nih.gov/pubmed/35194038
http://dx.doi.org/10.1038/s41467-022-28632-x
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author Harris, Paul David
Narducci, Alessandra
Gebhardt, Christian
Cordes, Thorben
Weiss, Shimon
Lerner, Eitan
author_facet Harris, Paul David
Narducci, Alessandra
Gebhardt, Christian
Cordes, Thorben
Weiss, Shimon
Lerner, Eitan
author_sort Harris, Paul David
collection PubMed
description Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H(2)MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H(2)MM (mpH(2)MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH(2)MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH(2)MM facilitates the identification and quantification of biomolecular sub-populations and their origin.
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spelling pubmed-88639872022-03-17 Multi-parameter photon-by-photon hidden Markov modeling Harris, Paul David Narducci, Alessandra Gebhardt, Christian Cordes, Thorben Weiss, Shimon Lerner, Eitan Nat Commun Article Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H(2)MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H(2)MM (mpH(2)MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH(2)MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH(2)MM facilitates the identification and quantification of biomolecular sub-populations and their origin. Nature Publishing Group UK 2022-02-22 /pmc/articles/PMC8863987/ /pubmed/35194038 http://dx.doi.org/10.1038/s41467-022-28632-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Harris, Paul David
Narducci, Alessandra
Gebhardt, Christian
Cordes, Thorben
Weiss, Shimon
Lerner, Eitan
Multi-parameter photon-by-photon hidden Markov modeling
title Multi-parameter photon-by-photon hidden Markov modeling
title_full Multi-parameter photon-by-photon hidden Markov modeling
title_fullStr Multi-parameter photon-by-photon hidden Markov modeling
title_full_unstemmed Multi-parameter photon-by-photon hidden Markov modeling
title_short Multi-parameter photon-by-photon hidden Markov modeling
title_sort multi-parameter photon-by-photon hidden markov modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863987/
https://www.ncbi.nlm.nih.gov/pubmed/35194038
http://dx.doi.org/10.1038/s41467-022-28632-x
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