Multi-parameter photon-by-photon hidden Markov modeling

Paul David Harris, Alessandra Narducci, Christian Gebhardt, Thorben Cordes, Shimon Weiss, Eitan Lerner

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H2MM) 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 H2MM (mpH2MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH2MM 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 mpH2MM facilitates the identification and quantification of biomolecular sub-populations and their origin.

Original languageEnglish
Article number1000
JournalNature Communications
Volume13
Issue number1
DOIs
StatePublished - 22 Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Funding

We thank Gregor Hagelücken and Martin Peter from the Institute of Structural Biology (University of Bonn, GER) for providing YopO. We would like to thank Robert Quast and Emmanuel Margeat for insightful discussions regarding the implementation of mpHMM for the analysis of 4-detector nsALEX measurements (2-color smFRET, with fluorescence anisotropies), based on their existing data. We would also like to thank Demain Lieberman for his helpful discussion regarding implementation of HMM code, and Bill Harris for his help in enabling the H2MM_C code to work on Windows and Linux. This paper was supported by the National Institutes of Health (NIH, grant R01 GM130942 to S.W. and E.L. as a subaward), the National Science Foundation (NSF, grants 1818147 and 1842951 to S.W.), the Human Frontiers Science Program (HFSP, grant RGP0061/2019 to S.W.), the Israel Science Foundation (ISF, grant 3565/20 to E.L., within the KillCorona – Curbing Coronavirus Research Program), the Milner Fund (to E.L.), and the Hebrew University of Jerusalem (start-up funds to E.L.). Work in the lab of T.C. was financed by Deutsche Forschungsgemeinschaft (SFB863, project A13 and GRK2062, project C03), an ERC Starting Grant (No. 638536 – SM-IMPORT to T.C.) and by the Center of Nanoscience Munich (CeNS). 2 2

FundersFunder number
Center of Nanoscience Munich
National Science Foundation1818147
National Institutes of Health
Foundation for the National Institutes of Health
National Institute of General Medical SciencesR01GM130942
Directorate for Biological Sciences1842951
European Commission638536 – SM-IMPORT
Human Frontier Science ProgramRGP0061/2019
Deutsche ForschungsgemeinschaftGRK2062, SFB863
Hebrew University of Jerusalem
Israel Science Foundation3565/20

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