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

• Published in: Nature communications Vol. 13; no. 1; p. 5402
• Main Authors: Götz, Markus, Barth, Anders, Bohr, Søren S.-R., Börner, Richard, Chen, Jixin, Cordes, Thorben, Erie, Dorothy A., Gebhardt, Christian, Hadzic, Mélodie C. A. S., Hamilton, George L., Hatzakis, Nikos S., Hugel, Thorsten, Kisley, Lydia, Lamb, Don C., de Lannoy, Carlos, Mahn, Chelsea, Dunukara, Dushani, de Ridder, Dick, Sanabria, Hugo, Schimpf, Julia, Seidel, Claus A. M., Sigel, Roland K. O., Sletfjerding, Magnus Berg, Thomsen, Johannes, Vollmar, Leonie, Wanninger, Simon, Weninger, Keith R., Xu, Pengning, Schmid, Sonja
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.09.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 14/33; 631/114/2415; 631/1647/527/2047; 631/57/2265; 631/92/56; Benchmarking; Benchmarks; Biomolecules; Equilibrium; Experimental data; Experiments; Fluorescence resonance energy transfer; Fluorescence Resonance Energy Transfer - methods; Heterogeneity; Humanities and Social Sciences; Kinetics; Laboratories; Models, Theoretical; multidisciplinary; Noise levels; Rate constants; Science; Science (multidisciplinary); Target recognition
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-33023-3

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. The ability to infer quantitative kinetic information from single-molecule FRET (smFRET) data can be challenging. Here the authors perform a blind benchmark study assessing different analysis tools used to infer kinetic rate constants from smFRET trajectories, testing on simulated and experimental data.