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

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, Thorsten Hugel, Lydia Kisley, Don C. Lamb, Carlos Lannoy, Chelsea Mahn, Dushani Dunukara, Dick Ridder, Hugo Sanabria, Julia Schimpf, Claus A. M. Seidel, Roland K. O. Sigel, Magnus Berg Sletfjerding, Johannes Thomsen, Leonie Vollmar, Simon Wanninger, Keith R. Weninger, Pengning Xu and Sonja Schmid ()
Additional contact information
Markus Götz: Univ Montpellier
Anders Barth: Heinrich-Heine-Universität, Universitätsstr. 1
Søren S.-R. Bohr: University of Copenhagen
Richard Börner: University of Zurich
Jixin Chen: Ohio University
Thorben Cordes: Ludwig-Maximilians-Universität München
Dorothy A. Erie: University of North Carolina
Christian Gebhardt: Ludwig-Maximilians-Universität München
Mélodie C. A. S. Hadzic: University of Zurich
George L. Hamilton: Clemson University
Nikos S. Hatzakis: University of Copenhagen
Thorsten Hugel: University of Freiburg
Lydia Kisley: 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
Claus A. M. Seidel: Heinrich-Heine-Universität, Universitätsstr. 1
Roland K. O. Sigel: University of Zurich
Magnus Berg Sletfjerding: University of Copenhagen
Johannes Thomsen: University of Copenhagen
Leonie Vollmar: 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

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33023-3

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DOI: 10.1038/s41467-022-33023-3

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