RNA is commonly believed to undergo a number of sequential folding steps before reaching its functional fold, i.e., the global minimum in the free energy landscape. However, there is accumulating evidence that several functional conformations are often in coexistence, corresponding to multiple (local) minima in the folding landscape. Here we use the 5′-exon-intron recognition duplex of a self-splicing ribozyme as a model system to study the influence of Mg 2+ NA folding is a hierarchical process that depends on the sequential formation of secondary and tertiary structures. As the RNA phosphate-sugar backbone is negatively charged, structural compaction creates electrostatic repulsion, which must be overcome by positive charges. The majority of negative charges are nonspecifically screened by the ion atmosphere, typically a set of dynamically exchanging M + ions (1). An estimated 10-20% of negative charge is, however, compensated by M n+ that bind site-specifically to the RNA molecule, in particular, Mg 2+ (2). One RNA molecule that is known to harbor several specific M 2+ binding sites is the self-splicing group II intron Sc.ai5γ from the yeast mitochondrial cox1 (cytochrome oxidase 1) gene (3). It is one of the largest known RNA enzymes, and both its folding pathway and catalysis are strictly dependent on Mg 2+. In turn, the splicing reaction is inhibited by small amounts of Ca 2+ (4). Site specificity of the two sequential transesterfication reactions is ensured by proper base pairing between distal exon-binding sites (5′ cleavage, EBS1 and 2; 3′ cleavage, EBS3) and intron-binding sites (IBS1, 2, and 3) (5).Single-molecule Förster resonance energy transfer (smFRET), i.e., distance-dependent energy transfer between a single pair of fluorophores, is ideally suited to study the cation-dependent conformational dynamics of single RNA molecules (6, 7). If different conformations lead to distinctly different transfer efficiencies, smFRET unveils the entire folding pathway, reports on the relative occurrence of all conformations present in the ensemble, and provides detailed information on the rates at which they interconvert (7). This is important because simple two-state folding is rarely observed in experimental data (8, 9). Rather, the vast conformational space sampled by biomolecules often results not only in folding intermediates but also in kinetic traps and/or multiple native states. In an smFRET experiment, individual molecules consequently display different behaviors that may or may not persist over the observation period (10). Heterogeneity has been precedented for a number of RNA molecules, including group I introns (11, 12), the hairpin ribozyme (13-17), and RNase P RNA (18). In addition, heterogeneity has been reported for different . However, the molecular basis of the phenomenon is often enigmatic, and its quantitative characterization is challenging (21).Here we use the 5′-exon-intron recognition site of the Sc.ai5γ ribozyme to study Mg 2+ -and Ca 2+ -mediated RNA-RNA structure formation by smFRET. ...

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/.

Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique to probe biomolecular structure and dynamics. A popular implementation of smFRET consists of recording fluorescence intensity time traces of surface-immobilized, chromophore-tagged molecules. This approach generates large and complex data sets, the analysis of which is to date not standardized. Here, we address a key challenge in smFRET data analysis: the generation of thermodynamic and kinetic models that describe with statistical rigor the behavior of FRET trajectories recorded from surface-tethered biomolecules in terms of the number of FRET states, the corresponding mean FRET values, and the kinetic rates at which they interconvert. For this purpose, we first perform Monte Carlo simulations to generate smFRET trajectories, in which a relevant space of experimental parameters is explored. Then, we provide an account on current strategies to achieve such model selection, as well as a quantitative assessment of their performances. Specifically, we evaluate the performance of each algorithm (change-point analysis, STaSI, HaMMy, vbFRET, and ebFRET) with respect to accuracy, reproducibility, and computing time, which yields a range of algorithm-specific referential benchmarks for various data qualities. Data simulation and analysis were performed with our MATLAB-based multifunctional analysis software for handling smFRET data (MASH-FRET).

Single-molecule microscopy has become a widely used technique in (bio)physics and (bio)chemistry. A popular implementation is single-molecule Förster Resonance Energy Transfer (smFRET), for which total internal reflection fluorescence microscopy is frequently combined with camera-based detection of surface-immobilized molecules. Camera-based smFRET experiments generate large and complex datasets and several methods for video processing and analysis have been reported. As these algorithms often address similar aspects in video analysis, there is a growing need for standardized comparison. Here, we present a Matlab-based software (MASH-FRET) that allows for the simulation of camera-based smFRET videos, yielding standardized data sets suitable for benchmarking video processing algorithms. The software permits to vary parameters that are relevant in cameras-based smFRET, such as video quality, and the properties of the system under study. Experimental noise is modeled taking into account photon statistics and camera noise. Finally, we survey how video test sets should be designed to evaluate currently available data analysis strategies in camera-based sm fluorescence experiments. We complement our study by pre-optimizing and evaluating spot detection algorithms using our simulated video test sets.

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.