1. Inferring kinetic rate constants from single-molecule FRET trajectories – a blind benchmark of kinetic analysis tools
- Author
-
M. C. A. S. Hadzic, C. de Lannoy, Don C. Lamb, D. Dunukara, R. K. O. Sigel, George L. Hamilton, Sonja Schmid, Hugo Sanabria, D. A. Erie, Pengning Xu, L. Kisley, J. Schimpf, Johannes Thomsen, Thorsten Hugel, Nikos S. Hatzakis, C. A. M. Seidel, M. Götz, Simon Wanninger, Thorben Cordes, Keith Weninger, C. Mahn, J. Chen, Christian Gebhardt, Anders Barth, Soeren S-R Bohr, L. Vollmar, R. Börner, Magnus Berg Sletfjerding, and Dick de Ridder
- Subjects
0303 health sciences ,Kinetic information ,Computer science ,Kinetic analysis ,Experimental data ,Single-molecule FRET ,010402 general chemistry ,01 natural sciences ,Model complexity ,0104 chemical sciences ,03 medical and health sciences ,Benchmark (computing) ,Analysis tools ,Biological system ,Kinetic rate constant ,030304 developmental biology - 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 tested 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.
- Published
- 2021
- Full Text
- View/download PDF