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Multi-parameter photon-by-photon hidden Markov modeling

View ORCID ProfilePaul David Harris, Shimon Weiss, View ORCID ProfileEitan Lerner
doi: https://doi.org/10.1101/2021.04.08.439035
Paul David Harris
1Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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Shimon Weiss
2Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los Angeles, California, United States
3Department of Physiology, CaliforniaNanoSystems Institute, University of California, Los Angeles, California, United States
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Eitan Lerner
1Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
4The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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  • For correspondence: eitan.lerner@mail.huji.ac.il
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Abstract

Single molecule FRET (smFRET) is a useful tool for studying biomolecular sub-populations and their dynamics. Advanced smFRET-based techniques often track multiple parameters simultaneously, increasing the information content of the measurement. Photon-by-photon hidden Markov modelling (H2MM) is a smFRET analysis tool that quantifies FRET dynamics of single biomolecules, even if they occur in sub-milliseconds. However, sub-populations can be characterized by additional experimentally-derived parameters other than the FRET efficiency. We introduce multi-parameter H2MM (mpH2MM) that identifies sub-populations and their transition dynamics based on multiple experimentally-derived parameters, simultaneously. We show the use of this tool in deciphering the number of underlying sub-populations, their mean characteristics and the rate constants of their transitions for a DNA hairpin exhibiting milliseconds FRET dynamics, and for the RNA polymerase promoter open complex exhibiting sub-millisecond FRET dynamics of the transcription bubble. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations in smFRET measurements that are otherwise difficult to identify. Finally we provide the means to use mpH2MM in analyzing FRET dynamics in advanced multi-color smFRET-based measurements.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* harripd{at}gmail.com

  • ↵† eitan.lerner{at}mail.huji.ac.il

  • https://doi.org/10.5281/zenodo.4671393

  • https://doi.org/10.5281/zenodo.4671440

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 10, 2021.
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Multi-parameter photon-by-photon hidden Markov modeling
Paul David Harris, Shimon Weiss, Eitan Lerner
bioRxiv 2021.04.08.439035; doi: https://doi.org/10.1101/2021.04.08.439035
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Multi-parameter photon-by-photon hidden Markov modeling
Paul David Harris, Shimon Weiss, Eitan Lerner
bioRxiv 2021.04.08.439035; doi: https://doi.org/10.1101/2021.04.08.439035

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