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

Author:

Götz MarkusORCID,Barth AndersORCID,Bohr Søren S.-R.,Börner RichardORCID,Chen JixinORCID,Cordes Thorben,Erie Dorothy A.,Gebhardt Christian,Hadzic Mélodie C. A. S.,Hamilton George L.ORCID,Hatzakis Nikos S.ORCID,Hugel ThorstenORCID,Kisley LydiaORCID,Lamb Don C.ORCID,de Lannoy CarlosORCID,Mahn Chelsea,Dunukara DushaniORCID,de Ridder DickORCID,Sanabria HugoORCID,Schimpf Julia,Seidel Claus A. M.ORCID,Sigel Roland K. O.ORCID,Sletfjerding Magnus BergORCID,Thomsen Johannes,Vollmar Leonie,Wanninger Simon,Weninger Keith R.,Xu Pengning,Schmid SonjaORCID

Abstract

AbstractSingle-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.

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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