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A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

Author

Listed:
  • Markus Götz

    (Univ Montpellier
    PicoQuant GmbH, Rudower Chaussee 29)

  • Anders Barth

    (Heinrich-Heine-Universität, Universitätsstr. 1
    Delft University of Technology)

  • Søren S.-R. Bohr

    (University of Copenhagen
    University of Copenhagen)

  • Richard Börner

    (University of Zurich
    University of Applied Sciences Mittweida)

  • Jixin Chen

    (Ohio University)

  • Thorben Cordes

    (Ludwig-Maximilians-Universität München)

  • Dorothy A. Erie

    (University of North Carolina
    University of North Carolina)

  • Christian Gebhardt

    (Ludwig-Maximilians-Universität München)

  • Mélodie C. A. S. Hadzic

    (University of Zurich)

  • George L. Hamilton

    (Clemson University
    New York University School of Medicine)

  • Nikos S. Hatzakis

    (University of Copenhagen
    University of Copenhagen)

  • Thorsten Hugel

    (University of Freiburg
    University of Freiburg)

  • Lydia Kisley

    (Case Western Reserve University
    Case Western Reserve University)

  • Don C. Lamb

    (Ludwig Maximilians-Universität München)

  • Carlos Lannoy

    (Wageningen University)

  • Chelsea Mahn

    (North Carolina State University)

  • Dushani Dunukara

    (Case Western Reserve University)

  • Dick Ridder

    (Wageningen University)

  • Hugo Sanabria

    (Clemson University)

  • Julia Schimpf

    (University of Freiburg
    University of Freiburg)

  • Claus A. M. Seidel

    (Heinrich-Heine-Universität, Universitätsstr. 1)

  • Roland K. O. Sigel

    (University of Zurich)

  • Magnus Berg Sletfjerding

    (University of Copenhagen
    University of Copenhagen)

  • Johannes Thomsen

    (University of Copenhagen
    University of Copenhagen)

  • Leonie Vollmar

    (University of Freiburg
    University of Freiburg)

  • Simon Wanninger

    (Ludwig Maximilians-Universität München)

  • Keith R. Weninger

    (North Carolina State University)

  • Pengning Xu

    (North Carolina State University)

  • Sonja Schmid

    (Wageningen University)

Abstract

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

Suggested Citation

  • Markus Götz & Anders Barth & Søren S.-R. Bohr & Richard Börner & Jixin Chen & Thorben Cordes & Dorothy A. Erie & Christian Gebhardt & Mélodie C. A. S. Hadzic & George L. Hamilton & Nikos S. Hatzakis &, 2022. "A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33023-3
    DOI: 10.1038/s41467-022-33023-3
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    References listed on IDEAS

    as
    1. Max Greenfeld & Dmitri S Pavlichin & Hideo Mabuchi & Daniel Herschlag, 2012. "Single Molecule Analysis Research Tool (SMART): An Integrated Approach for Analyzing Single Molecule Data," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-12, February.
    2. Paul David Harris & Alessandra Narducci & Christian Gebhardt & Thorben Cordes & Shimon Weiss & Eitan Lerner, 2022. "Multi-parameter photon-by-photon hidden Markov modeling," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. David Kelly & Mark Dillingham & Andrew Hudson & Karoline Wiesner, 2012. "A New Method for Inferring Hidden Markov Models from Noisy Time Sequences," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
    4. Sinan Kilic & Suren Felekyan & Olga Doroshenko & Iuliia Boichenko & Mykola Dimura & Hayk Vardanyan & Louise C. Bryan & Gaurav Arya & Claus A. M. Seidel & Beat Fierz, 2018. "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    5. Katherine Henzler-Wildman & Dorothee Kern, 2007. "Dynamic personalities of proteins," Nature, Nature, vol. 450(7172), pages 964-972, December.
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    Cited by:

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    2. Hyun Huh & Jiayu Shen & Yogeeshwar Ajjugal & Aparna Ramachandran & Smita S. Patel & Sang-Hyuk Lee, 2024. "Sequence-specific dynamic DNA bending explains mitochondrial TFAM’s dual role in DNA packaging and transcription initiation," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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