Abstract
Time-binned single-molecule Förster resonance energy transfer (smFRET) experiments with surface-tethered nucleic acids or proteins permit to follow folding and catalysis of single molecules in real-time. Due to the intrinsically low signal-to-noise ratio (SNR) in smFRET time traces, research over the past years has focused on the development of new methods to extract discrete states (conformations) from noisy data. However, limited observation time typically leads to pronounced cross-sample variability, i.e., single molecules display differences in the relative population of states and the corresponding conversion rates. Quantification of cross-sample variability is necessary to perform statistical testing in order to assess whether changes observed in response to an experimental parameter (metal ion concentration, the presence of a ligand, etc.) are significant. However, such hypothesis testing has been disregarded to date, precluding robust biological interpretation. Here, we address this problem by a bootstrap-based approach to estimate the experimental variability. Simulated time traces are presented to assess the robustness of the algorithm in conjunction with approaches commonly used in thermodynamic and kinetic analysis of time-binned smFRET data. Furthermore, a pair of functionally important sequences derived from the self-cleaving group II intron Sc.ai5γ (d3'EBS1*/IBS1*) is used as a model system. Through statistical hypothesis testing, divalent metal ions are shown to have a statistically significant effect on both thermodynamic and kinetic aspects of their interaction. The Matlab source code used for analysis (bootstrap-based analysis of smFRET data, BOBA FRET), as well as a graphical user interface, is available via http://www.aci.uzh.ch/rna/.
Highlights
Forster Resonance Energy Transfer (FRET), distance-dependent energy transfer via a long-range dipole-dipole interaction, occurs between a donor fluorophore and an acceptor, which is typically a fluorophore [1]
Distributed noise was added to simulated time traces that were varied in length, separation of the FRET populations, ratio of the rate constants associated with conformational interconversion, and signal-to-noise ratio (SNR) (Figure S4)
The relative population of FRET states was quantified using four commonly used approaches: Gaussian fitting of normalized cumulated FRET histograms, the ratio of dwell times obtained by either thresholding or hidden Markov modeling (HMM) [25], and fractional integration after thresholding of normalized cumulated FRET histograms, respectively
Summary
Forster Resonance Energy Transfer (FRET), distance-dependent energy transfer via a long-range dipole-dipole interaction, occurs between a donor fluorophore and an acceptor, which is typically (but not necessarily) a fluorophore [1]. Experiments involving surface-tethered molecules can be conducted with the aforementioned confocal microscope setup [5], a widefield or total internal reflection geometry is typically used for excitation, followed by detection with a CCD camera, resulting in time-binned FRET trajectories [6,7]. Statistical analysis of such time-binned data is the objective of this article
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