Tuning a riboswitch response through structural extension of a pseudoknot

Structural and dynamic features of RNA folding landscapes represent critical aspects of RNA function in the cell and are particularly central to riboswitch-mediated control of gene expression. Here, using single-molecule fluorescence energy transfer imaging, we explore the folding dynamics of the preQ₁ class II riboswitch, an upstream mRNA element that regulates downstream encoded modification enzymes of queuosine biosynthesis. For reasons that are not presently understood, the classical pseudoknot fold of this system harbors an extra stem–loop structure within its 3′-terminal region immediately upstream of the Shine–Dalgarno sequence that contributes to formation of the ligand-bound state. By imaging ligand-dependent preQ₁ riboswitch folding from multiple structural perspectives, we reveal that the extra stem–loop strongly influences pseudoknot dynamics in a manner that decreases its propensity to spontaneously fold and increases its responsiveness to ligand binding. We conclude that the extra stem–loop sensitizes this RNA to broaden the dynamic range of the ON/OFF regulatory switch.A variety of small metabolites have been found to regulate gene expression in bacteria, fungi, and plants via direct interactions with distinct mRNA folds (1–4). In this form of regulation, the target mRNA typically undergoes a structural change in response to metabolite binding (5–9). These mRNA elements have thus been termed “riboswitches” and generally include both a metabolite-sensitive aptamer subdomain and an expression platform. For riboswitches that regulate the process of translation, the expression platform minimally consists of a ribosomal recognition site [Shine–Dalgarno (SD)]. In the simplest form, the SD sequence overlaps with the metabolite-sensitive aptamer domain at its downstream end. Representative examples include the S-adenosylmethionine class II (SAM-II) (10) and the S-adenosylhomocysteine (SAH) riboswitches (11, 12), as well as prequeuosine class I (preQ₁-I) and II (preQ₁-II) riboswitches (13, 14). The secondary structures of these four short RNA families contain a pseudoknot fold that is central to their gene regulation capacity. Although the SAM-II and preQ₁-I riboswitches fold into classical pseudoknots (15, 16), the conformations of the SAH (17) and preQ₁-II counterparts are more complex and include a structural extension that contributes to the pseudoknot architecture (14). Importantly, the impact and evolutionary significance of these “extra” stem–loop elements on the function of the SAH and preQ₁-II riboswitches remain unclear.PreQ₁ riboswitches interact with the bacterial metabolite 7-aminomethyl-7-deazaguanine (preQ₁), a precursor molecule in the biosynthetic pathway of queuosine, a modified base encountered at the wobble position of some transfer RNAs (14). The general biological significance of studying the preQ₁-II system stems from the fact that this gene-regulatory element is found almost exclusively in the Streptococcaceae bacterial family. Moreover, the preQ₁ metabolite is not generated in humans and has to be acquired from the environment (14). Correspondingly, the preQ₁-II riboswitch represents a putative target for antibiotic intervention. Although preQ₁ class I (preQ₁-I) riboswitches have been extensively investigated (18–28), preQ₁ class II (preQ₁-II) riboswitches have been largely overlooked despite the fact that a different mode of ligand binding has been postulated (14).The consensus sequence and the secondary structure model for the preQ₁-II motif (COG4708 RNA) (Fig. 1A) comprise ∼80–100 nt (14). The minimal Streptococcus pneumoniae R6 aptamer domain sequence binds preQ₁ with submicromolar affinity and consists of an RNA segment forming two stem–loops, P2 and P4, and a pseudoknot P3 (Fig. 1B). In-line probing studies suggest that the putative SD box (AGGAGA; Fig. 1) is sequestered by pseudoknot formation, which results in translational-dependent gene regulation of the downstream gene (14).Open in a separate windowFig. 1.PreQ₁ class II riboswitch. (A) Chemical structure of 7-aminomethyl-7-deazaguanosine (preQ₁); consensus sequence and secondary structure model for the COG4708 RNA motif (adapted from reference 14). Nucleoside presence and identity as indicated. (B) S. pneumoniae R6 preQ₁-II RNA aptamer investigated in this study. (C) Schematics of an H-type pseudoknot with generally used nomenclature for comparison.Here, we investigated folding and ligand recognition of the S. pneumoniae R6 preQ₁-II riboswitch, using complementary chemical, biochemical, and biophysical methods including selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE), mutational analysis experiments, 2-aminopurine fluorescence, and single-molecule fluorescence resonance energy transfer (smFRET) imaging. In so doing, we explored the structural and functional impact of the additional stem–loop element in the context of its otherwise “classical” H-type pseudoknot fold (29–32) (Fig. 1C). Our results reveal that the unique 3′-stem–loop element in the preQ₁-II riboswitch contributes to the process of SD sequestration, and thus the regulation of gene expression, by modulating both its intrinsic dynamics and its responsiveness to ligand binding.     

Transition paths,diffusive processes,and preequilibria of protein folding

Fundamental relationships between the thermodynamics and kinetics of protein folding were investigated using chain models of natural proteins with diverse folding rates by extensive comparisons between the distribution of conformations in thermodynamic equilibrium and the distribution of conformations sampled along folding trajectories. Consistent with theory and single-molecule experiment, duration of the folding transition paths exhibits only a weak correlation with overall folding time. Conformational distributions of folding trajectories near the overall thermodynamic folding/unfolding barrier show significant deviations from preequilibrium. These deviations, the distribution of transition path times, and the variation of mean transition path time for different proteins can all be rationalized by a diffusive process that we modeled using simple Monte Carlo algorithms with an effective coordinate-independent diffusion coefficient. Conformations in the initial stages of transition paths tend to form more nonlocal contacts than typical conformations with the same number of native contacts. This statistical bias, which is indicative of preferred folding pathways, should be amenable to future single-molecule measurements. We found that the preexponential factor defined in the transition state theory of folding varies from protein to protein and that this variation can be rationalized by our Monte Carlo diffusion model. Thus, protein folding physics is different in certain fundamental respects from the physics envisioned by a simple transition-state picture. Nonetheless, transition state theory can be a useful approximate predictor of cooperative folding speed, because the height of the overall folding barrier is apparently a proxy for related rate-determining physical properties.Protein folding is an intriguing phenomenon at the interface of physics and biology. In the early days of folding kinetics studies, folding was formulated almost exclusively in terms of mass-action rate equations connecting the folded, unfolded, and possibly, one or a few intermediate states (1, 2). With the advent of site-directed mutagenesis, the concept of free energy barriers from transition state theory (TST) (3) was introduced to interpret mutational data (4), and subsequently, it was adopted for the Φ-value analysis (5). Since the 1990s, the availability of more detailed experimental data (6), in conjunction with computational development of coarse-grained chain models, has led to an energy landscape picture of folding (7–15). This perspective emphasizes the diversity of microscopic folding trajectories, and it conceptualizes folding as a diffusive process (16–25) akin to the theory of Kramers (26).For two-state-like folding, the transition path (TP), i.e., the sequence of kinetic events that leads directly from the unfolded state to the folded state (27, 28), constitutes only a tiny fraction of a folding trajectory that spends most of the time diffusing, seemingly unproductively, in the vicinity of the free energy minimum of the unfolded state. The development of ultrafast laser spectroscopy (29, 30) and single-molecule (27, 28, 31) techniques have made it possible to establish upper bounds on the transition path time (t_TP) ranging from <200 and <10 μs by earlier (27) and more recent (28), respectively, direct single-molecule FRET to <2 μs (30) by bulk relaxation measurements. Consistent with these observations, recent extensive atomic simulations have also provided estimated t_TP values of the order of ∼1 μs (32, 33). These advances offer exciting prospects of characterizing the productive events along folding TPs.It is timely, therefore, to further the theoretical investigation of TP-related questions (19). To this end, we used coarse-grained C_α models (14) to perform extensive simulations of the folding trajectories of small proteins with 56- to 86-aa residues. These tractable models are useful, because despite significant progress, current atomic models cannot provide the same degree of sampling coverage for proteins of comparable sizes (32, 33). In addition to structural insights, this study provides previously unexplored vantage points to compare the diffusion and TST pictures of folding. Deviations of folding behaviors from TST predictions are not unexpected, because TST is mostly applicable to simple gas reactions; however, the nature and extent of the deviations have not been much explored. Our explicit-chain simulation data conform well to the diffusion picture but not as well to TST. In particular, the preexponential factors of the simulated folding rates exhibit a small but appreciable variation that depends on native topology. These findings and others reported below underscore the importance of single-molecule measurements (13, 27, 28, 31, 34, 35) in assessing the merits of proposed scenarios and organizing principles of folding (7–25, 36, 37).     

Understanding the kinetic mechanism of RNA single base pair formation

RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model.RNAs perform critical cellular functions at the level of gene expression and regulation (1–4). RNA functions are determined not only by RNA structure or structure motifs [e.g., tetraloop hairpins (5, 6)] but also by conformational distributions and dynamics and kinetics of conformational changes. For example, riboswitches can adopt different conformations in response to specific conditions of the cellular environment (7, 8). Understanding the kinetics, such as the rate and pathways for the conformational changes, is critical for deciphering the mechanism of RNA function (9–19). Extensive experimental and theoretical studies on RNA folding kinetics have provided significant insights into the kinetic mechanism of RNA functions (19–36). However, due to the complexity of the RNA folding energy landscape (37–46) and the limitations of experimental tools (47–55), many fundamental problems, including single base flipping and base pair formation and fraying, remain unresolved. These unsolved fundamental problems have hampered our ability to resolve other important issues, such as RNA hairpin and larger structure folding kinetics. Several key questions remain unanswered, such as whether the hairpin folding is rate-limited by the conformational search of the native base pairs, whose formation leads to fast downhill folding of the whole structure, or by the breaking of misfolded base pairs before refolding to the native structure (18, 19, 54–73).Motivated by the need to understand the basic steps of nucleic acids folding, Hagan et al. (74) performed forty-three 200-ps unfolding trajectories at 400 K and identified both on- and off-pathway intermediates and two dominant unfolding pathways for a terminal C-G base pair in a DNA duplex. In one of the pathways, base pairing and stacking interactions are broken concomitantly, whereas in the other pathway, base stacking is broken after base pairing is disrupted. Furthermore, the unfolding requires that the Cyt diffuse away from the pairing Gua to a distance such that the C-G hydrogen bond cannot reform easily. More recently, Colizzi and Bussi (75) performed molecular dynamics (MD) pulling simulations for an RNA duplex and construct free energy landscape from the pulling simulation. The simulation showed that the base pair opening reaction starts with the unbinding of the 5′-base, followed by the unbinding of the 3′-base (i.e., the 5′-base is less stable than the 3′-base). These previous unfolding simulations offered significant insights into the pathways and transition states. However, as shown below, several important issues remain.One intriguing problem is the rate model for base pairing. There are currently three main types of models. In the first type of model, the barrier ΔG+‡ for closing a base pair is dominated by the entropic cost ΔS for positioning the nucleotides to the base-paired configuration and the barrier ΔG−‡ for opening a base pair is the enthalpic cost ΔH for disrupting the hydrogen bonds and base stacking interactions (18, 59, 60). In the second type of model, ΔG+‡ is the net free energy change for base pairing ΔG = ΔH ? TΔS and ΔG−‡ is zero (76, 77). In the third type of model, ΔG±‡=±ΔG/2 is used (78). In addition to the above three main types, other models, such as more sophisticated hybrid rate models, have been proposed (29).In this paper, we report a hybrid method (see Fig. 1) to investigate the single base pairing process. In contrast to the previous simulations for temperature- or force-induced unfolding reactions, we directly model the folding process here (i.e., the base pair closing process). Specifically, we use MD simulations to identify the conformational clusters. Based on the network of the conformational clusters as a reduced conformational ensemble, we apply kinetic Monte Carlo (KMC) and master equation (ME) methods to elucidate the detailed roles of base pairing and stacking interactions, as well as the roles of water and ions (79–82). The study reveals previously unidentified kinetics pathways, misfolded states, and rate-limiting steps. A clear understanding of the microscopic details of the elementary kinetic move is a prerequisite for further rigorous study of large-scale RNA kinetic studies. The method described here may provide a feasible way to develop a rate model for the base pair/stack-based kinetic move set. Furthermore, the mechanism of RNA single base folding may provide useful insights into many biologically significant processes, such as nucleotide flipping (83) in helicases and base pair fraying (84) (as the possible first step for nucleic duplex melting in nucleic acid enzymatic processes).Open in a separate windowFig. 1.(A) Folding of a single nucleotide (G1, red) from the unfolded (Left) to the native folded (Right) state. (B) Exhaustive sampling for the (discrete) conformations of the G1 nucleotide (Right) through enumeration of the torsion angles (formed by the blue bonds). (C) Schematic plot shows the trajectories on the energy landscape (depicted with two reaction coordinates for clarity) explored by the MD simulations. The lines, open circles, and hexagons denote the trajectories; the initial states; and the (centroid structures of the) clusters, respectively. (D) Conformational network based on six clusters. (E) The rmsds to the different clusters provide information about the structural changes in a MD trajectory.