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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-8863987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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