Cation-induced kinetic heterogeneity of the intron-exon recognition in single group II introns

Two riboswitch classes that share a common ligand-binding fold show major differences in the ability to accommodate mutations

Riboswitches are structured RNAs that sense small molecules to control expression. Prequeuosine1 (preQ1)-sensing riboswitches comprise three classes (I, II and III) that adopt distinct folds. Despite this difference, class II and III riboswitches each use 10 identical nucleotides to bind the preQ1 metabolite. Previous class II studies showed high sensitivity to binding-pocket mutations, which reduced preQ1 affinity and impaired function. Here, we introduced four equivalent mutations into a class III riboswitch, which maintained remarkably tight preQ1 binding. Co-crystal structures of each class III mutant showed compensatory interactions that preserve the fold. Chemical modification analysis revealed localized RNA flexibility changes for each mutant, but molecular dynamics (MD) simulations suggested that each mutation was not overtly destabilizing. Although impaired, class III mutants retained tangible gene-regulatory activity in bacteria compared to equivalent preQ1-II variants; mutations in the preQ1-pocket floor were tolerated better than wall mutations. Principal component analysis of MD trajectories suggested that the most functionally deleterious wall mutation samples different motions compared to wildtype. Overall, the results reveal that formation of compensatory interactions depends on the context of mutations within the overall fold and that functionally deleterious mutations can alter long-range correlated motions that link the riboswitch binding pocket with distal gene-regulatory sequences.

Watching ion-driven kinetics of ribozyme folding and misfolding caused by energetic and topological frustration one molecule at a time

Folding of ribozymes into well-defined tertiary structures usually requires divalent cations. How Mg2+ ions direct the folding kinetics has been a long-standing unsolved problem because experiments cannot detect the positions and dynamics of ions. To address this problem, we used molecular simulations to dissect the folding kinetics of the Azoarcus ribozyme by monitoring the path each molecule takes to reach the folded state. We quantitatively establish that Mg2+ binding to specific sites, coupled with counter-ion release of monovalent cations, stimulate the formation of secondary and tertiary structures, leading to diverse pathways that include direct rapid folding and trapping in misfolded structures. In some molecules, key tertiary structural elements form when Mg2+ ions bind to specific RNA sites at the earliest stages of the folding, leading to specific collapse and rapid folding. In others, the formation of non-native base pairs, whose rearrangement is needed to reach the folded state, is the rate-limiting step. Escape from energetic traps, driven by thermal fluctuations, occurs readily. In contrast, the transition to the native state from long-lived topologically trapped native-like metastable states is extremely slow. Specific collapse and formation of energetically or topologically frustrated states occur early in the assembly process.

Pressure-controlled microfluidics for automated single-molecule sample preparation

Sample preparation is a crucial step in single-molecule experiments and involves passivating the microfluidic sample chamber, immobilizing the molecules, and setting experimental buffer conditions. The efficiency of the experiment depends on the quality and speed of sample preparation, which is often performed manually and relies on the experience of the experimenter. This can result in inefficient use of single-molecule samples and time, especially for high-throughput applications. To address this, a pressure-controlled microfluidic system is proposed to automate single-molecule sample preparation. The hardware is based on microfluidic components from ElveFlow and is designed to be cost-effective and adaptable to various microscopy applications. The system includes a reservoir pressure adapter and a reservoir holder designed for additive manufacturing. Two flow chamber designs Ibidi µ-slide and Grace Bio-Labs HybriWell chamber are characterized, and the flow characteristics of the liquid at different volume flow rates V˙ are simulated using CFD-simulations and compared to experimental and theoretical values. The goal of this work is to establish a straightforward and robust system for single-molecule sample preparation that can increase the efficiency of experiments and reduce the bottleneck of manual sample preparation, particularly for high-throughput applications.

Moderate activity of RNA chaperone maximizes the yield of self-spliced pre-RNA in vivo

CYT-19 is a DEAD-box protein whose adenosine-triphosphate (ATP)-dependent helicase activity facilitates the folding of group I introns in precursor RNA (pre-RNA) of Neurospora crassa (N. crassa). In the process, they consume a substantial amount of ATP. While much of the mechanistic insight into CYT-19 activity has been gained through the studies on the folding of Tetrahymena group I intron ribozyme, the more biologically relevant issue, namely the effect of CYT-19 on the self-splicing of pre-RNA, remains largely unexplored. Here, we employ a kinetic network model, based on the generalized iterative annealing mechanism (IAM), to investigate the relation between CYT-19 activity, rate of ribozyme folding, and the kinetics of the self-splicing reaction. The network rate parameters are extracted by analyzing the recent biochemical data for CYT-19-facilitated folding of Tetrahymena ribozyme. We then build extended models to explore the metabolism of pre-RNA. We show that the timescales of chaperone-mediated folding of group I ribozyme and self-splicing reaction compete with each other. As a consequence, in order to maximize the self-splicing yield of group I introns in pre-RNA, the chaperone activity must be sufficiently large to unfold the misfolded structures, but not too large to unfold the native structures prior to the self-splicing event. We discover that despite the promiscuous action on structured RNAs, the helicase activity of CYT-19 on group I ribozyme gives rise to self-splicing yields that are close to the maximum.

Diffuse Ions Coordinate Dynamics in a Ribonucleoprotein Assembly

Proper ionic concentrations are required for the functional dynamics of RNA and ribonucleoprotein (RNP) assemblies. While experimental and computational techniques have provided many insights into the properties of chelated ions, less is known about the energetic contributions of diffuse ions to large-scale conformational rearrangements. To address this, we present a model that is designed to quantify the influence of diffuse monovalent and divalent ions on the dynamics of biomolecular assemblies. This model employs all-atom (non-H) resolution and explicit ions, where effective potentials account for hydration effects. We first show that the model accurately predicts the number of excess Mg²⁺ ions for prototypical RNA systems, at a level comparable to modern coarse-grained models. We then apply the model to a complete ribosome and show how the balance between diffuse Mg²⁺ and K⁺ ions can control the dynamics of tRNA molecules during translation. The model predicts differential effects of diffuse ions on the free-energy barrier associated with tRNA entry and the energy of tRNA binding to the ribosome. Together, this analysis reveals the direct impact of diffuse ions on the dynamics of an RNP assembly.

Artificial Intelligence Resolves Kinetic Pathways of Magnesium Binding to RNA

Magnesium is an indispensable cofactor in countless vital processes. In order to understand its functional role, the characterization of the binding pathways to biomolecules such as RNA is crucial. Despite the importance, a molecular description is still lacking since the transition from the water-mediated outer-sphere to the direct inner-sphere coordination is on the millisecond time scale and therefore out of reach for conventional simulation techniques. To fill this gap, we use transition path sampling to resolve the binding pathways and to elucidate the role of the solvent in the binding process. The results reveal that the molecular void provoked by the leaving phosphate oxygen of the RNA is immediately filled by an entering water molecule. In addition, water molecules from the first and second hydration shell couple to the concerted exchange. To capture the intimate solute-solvent coupling, we perform a committor analysis as the basis for a machine learning algorithm that derives the optimal deep learning model from thousands of scanned architectures using hyperparameter tuning. The results reveal that the properly optimized deep network architecture recognizes the important solvent structures, extracts the relevant information, and predicts the commitment probability with high accuracy. Our results provide detailed insights into the solute-solvent coupling which is ubiquitous for kosmotropic ions and governs a large variety of biochemical reactions in aqueous solutions.

MASH-FRET: A Simplified Approach for Single-Molecule Multiplexing Using FRET

Multiplexed detection has been a big motivation in biomarker analysis as it not only saves cost and labor but also improves the reliability of diagnosis. Among the many approaches for multiplexed detection, fluorescence resonance energy transfer (FRET)-based multiplexing is gaining popularity particularly due to its low background and quantitative nature. Although several FRET-based approaches have been developed for multiplexing, they require either multiple FRET pairs in combination with multiple excitation sources or complicated algorithms to accurately assign signals for individual FRET pairs. Therefore, the need for multiple FRET pairs and multiple excitation sources not only complicates the experimental design but also increases the cost and labor. In this regard, multiplexed sensing by tuning the interdye distance of a single FRET pair could be an ideal solution if identification of multiple FRET efficiencies in a single imaging is possible. Here, implementing a program called MASH-FRET, we evaluated the rigor and capability of this program in identifying seemingly overlapped FRET populations obtained from a multiplexed detection experiment using a single FRET pair. Through MASH-FRET-enabled bootstrap-based analysis of FRET data (also called BOBA-FRET), we demonstrated that the resolution and statistical confidence of the poorly resolved or even unresolved FRET populations can be readily determined. Using simulated FRET data, we further demonstrated that the program can easily identify FRET populations separated by ∼0.1 in mean FRET values, indicating an upper limit of ∼9-fold multiplexing without the need for complicated labeling schemes and multiexcitation sources. Therefore, this paper presents a data analysis approach on an existing platform that has a great potential to simplify the technological needs for multiplexing and to broaden the scope of FRET-based single-molecule analyses.

Kinetic pathways of water exchange in the first hydration shell of magnesium

Water exchange between the coordination shells of metal cations in aqueous solutions is fundamental in understanding their role in biochemical processes. Despite the importance, the microscopic mechanism of water exchange in the first hydration shell of Mg²⁺ has not been resolved since the exchange dynamics is out of reach for conventional all-atom simulations. To overcome this challenge, transition path sampling is applied to resolve the kinetic pathways, to characterize the reaction mechanism and to provide an accurate estimate of the exchange rate. The results reveal that water exchange involves the concerted motion of two exchanging water molecules and the collective rearrangement of all water molecules in the first hydration shell. Using a recently developed atomistic model for Mg²⁺, water molecules remain in the first hydration shell for about 40 ms, a time considerably longer compared to the 0.1 ms predicted by transition state theory based on the coordinates of a single water molecule. The discrepancy between these timescales arises from the neglected degrees of freedom of the second exchanging water molecule that plays a decisive role in the reaction mechanism. The approach presented here contributes molecular insights into the dynamics of water around metal cations and provides the basis for developing accurate atomistic models or for understanding complex biological processes involving metal cations.

Hofmeister Series for Metal-Cation-RNA Interactions: The Interplay of Binding Affinity and Exchange Kinetics

A large variety of physicochemical properties involving RNA depends on the type of metal cation present in solution. In order to gain microscopic insight into the origin of these ion specific effects, we apply molecular dynamics simulations to describe the interactions of metal cations and RNA. For the three most common ion binding sites on RNA, we calculate the binding affinities and exchange rates of eight different mono- and divalent metal cations. Our results reveal that binding sites involving phosphate groups preferentially bind metal cations with high charge density (such as Mg²⁺) in inner-sphere conformations while binding sites involving N7 or O6 atoms preferentially bind cations with low charge density (such as K⁺). The binding affinity therefore follows a direct Hofmeister series at the backbone but is reversed at the nucleobases leading to a high selectivity of ion binding sites on RNA. In addition, the exchange rates for cation binding cover almost 5 orders of magnitude, leading to a vastly different time scale for the lifetimes of contact pairs. Taken together, the site-specific binding affinities and the specific lifetime of contact pairs provide the microscopic explanation of ion specific effects observed in a wide variety of macroscopic RNA properties. Finally, combining the results from atomistic simulations with extended Poisson-Boltzmann theory allows us to predict the distribution of metal cations around double-stranded RNA at finite concentrations and to reproduce the results of ion counting experiments with good accuracy.

Metal ions and sugar puckering balance single-molecule kinetic heterogeneity in RNA and DNA tertiary contacts

The fidelity of group II intron self-splicing and retrohoming relies on long-range tertiary interactions between the intron and its flanking exons. By single-molecule FRET, we explore the binding kinetics of the most important, structurally conserved contact, the exon and intron binding site 1 (EBS1/IBS1). A comparison of RNA-RNA and RNA-DNA hybrid contacts identifies transient metal ion binding as a major source of kinetic heterogeneity which typically appears in the form of degenerate FRET states. Molecular dynamics simulations suggest a structural link between heterogeneity and the sugar conformation at the exon-intron binding interface. While Mg2+ ions lock the exon in place and give rise to long dwell times in the exon bound FRET state, sugar puckering alleviates this structural rigidity and likely promotes exon release. The interplay of sugar puckering and metal ion coordination may be an important mechanism to balance binding affinities of RNA and DNA interactions in general.

Encapsulation of Fluorescently Labeled RNAs into Surface-Tethered Vesicles for Single-Molecule FRET Studies in TIRF Microscopy

Imaging fluorescently labeled biomolecules on a single-molecule level is a well-established technique to follow intra- and intermolecular processes in time, usually hidden in the ensemble average. The classical approach comprises surface immobilization of the molecule of interest, which increases the risk of restricting the natural behavior due to surface interactions. Encapsulation of such biomolecules into surface-tethered phospholipid vesicles enables to follow one molecule at a time, freely diffusing and without disturbing surface interactions. Further, the encapsulation allows to keep reaction partners (reactants and products) in close proximity and enables higher temperatures otherwise leading to desorption of the direct immobilized biomolecules.Here, we describe a detailed protocol for the encapsulation of a catalytically active RNA starting from surface passivation over RNA encapsulation to data evaluation of single-molecule FRET experiments in TIRF microscopy. We present an optimized procedure that preserves RNA functionality and applies to investigations of, e.g., large ribozymes and RNAs, where direct immobilization is structurally not possible.

Dynamic coordination of two-metal-ions orchestrates λ-exonuclease catalysis

Metal ions at the active site of an enzyme act as cofactors, and their dynamic fluctuations can potentially influence enzyme activity. Here, we use λ-exonuclease as a model enzyme with two Mg²⁺ binding sites and probe activity at various concentrations of magnesium by single-molecule-FRET. We find that while Mg_A²⁺ and Mg_B²⁺ have similar binding constants, the dissociation rate of Mg_A²⁺ is two order of magnitude lower than that of Mg_B²⁺ due to a kinetic-barrier-difference. At physiological Mg²⁺ concentration, the Mg_B²⁺ ion near the 5'-terminal side of the scissile phosphate dissociates each-round of degradation, facilitating a series of DNA cleavages via fast product-release concomitant with enzyme-translocation. At a low magnesium concentration, occasional dissociation and slow re-coordination of Mg_A²⁺ result in pauses during processive degradation. Our study highlights the importance of metal-ion-coordination dynamics in correlation with the enzymatic reaction-steps, and offers insights into the origin of dynamic heterogeneity in enzymatic catalysis.

Reliable State Identification and State Transition Detection in Fluorescence Intensity-Based Single-Molecule Förster Resonance Energy-Transfer Data

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).

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Simulations of camera-based single-molecule fluorescence experiments

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.

Physics-based all-atom modeling of RNA energetics and structure

The database of RNA sequences is exploding, but knowledge of energetics, structures, and dynamics lags behind. All-atom computational methods, such as molecular dynamics, hold promise for closing this gap. New algorithms and faster computers have accelerated progress in improving the reliability and accuracy of predictions. Currently, the methods can facilitate refinement of experimentally determined nuclear magnetic resonance and x-ray structures, but are 'unreliable' for predictions based only on sequence. Much remains to be discovered, however, about the many molecular interactions driving RNA folding and the best way to approximate them quantitatively. The large number of parameters required means that a wide variety of experimental results will be required to benchmark force fields and different approaches. As computational methods become more reliable and accessible, they will be used by an increasing number of biologists, much as x-ray crystallography has expanded. Thus, many fundamental physical principles underlying the computational methods are described. This review presents a summary of the current state of molecular dynamics as applied to RNA. It is designed to be helpful to students, postdoctoral fellows, and faculty who are considering or starting computational studies of RNA. WIREs RNA 2017, 8:e1422. doi: 10.1002/wrna.1422.

An atomistic view on carbocyanine photophysics in the realm of RNA

Carbocyanine dyes have a long-standing tradition in fluorescence imaging and spectroscopy, due to their photostability and large spectral separation between individual dye species. Herein, we explore the versatility of cyanine dyes to probe the dynamics of nucleic acids and we report on the interrelation of fluorophores, RNA, and metal ions, namely K⁺ and Mg²⁺. Photophysical parameters including the fluorescence lifetime, quantum yield and dynamic anisotropy are monitored as a function of the nucleic acid composition, conformation, and metal ion abundance. Occasional excursions to a non-fluorescent cis-state hint at the remarkable sensitivity of carbocyanines to their local environment. Comparison of time-correlated single photon experiments with all-atom molecular dynamics simulations demonstrate that the propensity of photoisomerization is dictated by sterical constraints imposed on the fluorophore. Structural features in the vicinity of the dye play a crucial role in RNA recognition and have far-reaching implications on the mobility of the fluorescent probe. An atomic level description of the mutual interactions will ultimately benefit the quantitative interpretation of single-molecule FRET measurements on large RNA systems.

Decoding Single Molecule Time Traces with Dynamic Disorder

Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm—Variational Bayes-double chain Markov model (VB-DCMM)—to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na⁺ solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.

We have developed a new algorithm to better decode single molecule data with dynamic disorder. Our new algorithm, which represents a substantial improvement over other methodologies, can detect the presence of dynamic disorder in each trajectory and quantify the kinetic characteristics of underlying energy landscape. As a model system, we applied our algorithm to the single molecule FRET time traces of H-DNA. While duplex-triplex transitions of H-DNA are conventionally interpreted in terms of two-state kinetics, slowly varying dynamic patterns corresponding to hidden internal states can also be identified from the individual time traces. Our algorithm reveals that at least 4 distinct internal states are required to correctly interpret the data.

Development of Site-Specific Mg(2+)-RNA Force Field Parameters: A Dream or Reality? Guidelines from Combined Molecular Dynamics and Quantum Mechanics Simulations

The vital contribution of Mg²⁺ ions to RNA biology is challenging to dissect at the experimental level. This calls for the integrative support of atomistic simulations, which at the classical level are plagued by limited accuracy. Indeed, force fields intrinsically neglect nontrivial electronic effects that Mg²⁺ exerts on its surrounding ligands in varying RNA coordination environments. Here, we present a combined computational study based on classical molecular dynamics (MD) and Density Functional Theory (DFT) calculations, aimed at characterizing (i) the performance of five Mg²⁺ force field (FF) models in RNA systems and (ii) how charge transfer and polarization affect the binding of Mg²⁺ ions in different coordination motifs. As a result, a total of ∼2.5 μs MD simulations (100/200 ns for each run) for two prototypical Mg²⁺-dependent ribozymes showed remarkable differences in terms of populations of inner-sphere coordination site types. Most importantly, complementary DFT calculations unveiled that differences in charge transfer and polarization among recurrent Mg²⁺-RNA coordination motifs are surprisingly small. In particular, the charge of the Mg²⁺ ions substantially remains constant through different coordination sites, suggesting that the common philosophy of developing site-specific Mg²⁺ ion parameters is not in line with the physical origin of the Mg²⁺-RNA MD simulations inaccuracies. Overall, this study constitutes a guideline for an adept use of current Mg²⁺ models and provides novel insights for the rational development of next-generation Mg²⁺ FFs to be employed for atomistic simulations of RNA.

Directly measuring single-molecule heterogeneity using force spectroscopy

One of the most intriguing results of single-molecule experiments on proteins and nucleic acids is the discovery of functional heterogeneity: the observation that complex cellular machines exhibit multiple, biologically active conformations. The structural differences between these conformations may be subtle, but each distinct state can be remarkably long-lived, with interconversions between states occurring only at macroscopic timescales, fractions of a second or longer. Although we now have proof of functional heterogeneity in a handful of systems-enzymes, motors, adhesion complexes-identifying and measuring it remains a formidable challenge. Here, we show that evidence of this phenomenon is more widespread than previously known, encoded in data collected from some of the most well-established single-molecule techniques: atomic force microscopy or optical tweezer pulling experiments. We present a theoretical procedure for analyzing distributions of rupture/unfolding forces recorded at different pulling speeds. This results in a single parameter, quantifying the degree of heterogeneity, and also leads to bounds on the equilibration and conformational interconversion timescales. Surveying 10 published datasets, we find heterogeneity in 5 of them, all with interconversion rates slower than 10 s(-1) Moreover, we identify two systems where additional data at realizable pulling velocities is likely to find a theoretically predicted, but so far unobserved crossover regime between heterogeneous and nonheterogeneous behavior. The significance of this regime is that it will allow far more precise estimates of the slow conformational switching times, one of the least understood aspects of functional heterogeneity.

Mg(2+) shifts ligand-mediated folding of a riboswitch from induced-fit to conformational selection

Bacterial riboswitches couple small-molecule ligand binding to RNA conformational changes that widely regulate gene expression, rendering them potential targets for antibiotic intervention. Despite structural insights, the ligand-mediated folding mechanisms of riboswitches are still poorly understood. Using single-molecule fluorescence resonance energy transfer (smFRET), we have investigated the folding mechanism of an H-type pseudoknotted preQ1 riboswitch in dependence of Mg(2+) and three ligands of distinct affinities. We show that, in the absence of Mg(2+), both weakly and strongly bound ligands promote pseudoknot docking through an induced-fit mechanism. By contrast, addition of as low as 10 μM Mg(2+) generally shifts docking toward conformational selection by stabilizing a folded-like conformation prior to ligand binding. Supporting evidence from transition-state analysis further highlights the particular importance of stacking interactions during induced-fit and of specific hydrogen bonds during conformational selection. Our mechanistic dissection provides unprecedented insights into the intricate synergy between ligand- and Mg(2+)-mediated RNA folding.

BOBA FRET: bootstrap-based analysis of single-molecule FRET data

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/.