Almost all RNAs can fold to form extensive base-paired secondary structures. Many of these structures then modulate numerous fundamental elements of gene expression. Deducing these structure-function relationships requires that it be possible to predict RNA secondary structures accurately. However, RNA secondary structure prediction for large RNAs, such that a single predicted structure for a single sequence reliably represents the correct structure, has remained an unsolved problem. Here, we demonstrate that quantitative, nucleotide-resolution information from a SHAPE experiment can be interpreted as a pseudo-free energy change term and used to determine RNA secondary structure with high accuracy. Free energy minimization, by using SHAPE pseudofree energies, in conjunction with nearest neighbor parameters, predicts the secondary structure of deproteinized Escherichia coli 16S rRNA (>1,300 nt) and a set of smaller RNAs (75-155 nt) with accuracies of up to 96 -100%, which are comparable to the best accuracies achievable by comparative sequence analysis.RNA secondary structure ͉ prediction ͉ ribosome ͉ pseudo-free energy ͉ dynamic programming E ssentially all RNA molecules, even those with seemingly random sequences, have the ability to form extensive internal base pairs (1-3). This internal structure has profound consequences for RNA function. At large scales, long RNAs fold to form complex regulatory motifs like those found in the 5Ј and 3Ј untranslated regions of mRNAs and viral genomes and in large structured RNAs like ribozymes (4). On small scales, the extent of local structure over regions spanning 10-50 nt modulates whether an RNA motif can function in translation initiation by the ribosome, is accessible for interaction with the splicing machinery, or binds small siRNAs and miRNAs (5-7).To understand these fundamental cellular processes, it must be possible to reliably establish the structure of an RNA based on a single sequence. Accurate RNA secondary structures reflecting a single biological state are essential to deduce structure-function relationships in the many RNAs (i) for which a structure cannot be inferred by comparative analysis, (ii) that switch between distinct base-paired conformations to carry out their biological function, or (iii) that are in the process of folding to a functional state.Two broad classes of approaches are used to score RNA secondary structure predictions for single sequences: empirical freeenergy parameters (7) and knowledge based (8-10). The current best-performing algorithms achieve a sensitivity (percentage of known base pairs predicted correctly) of 40-70% (8-12). Prediction accuracies are higher for shorter RNAs, for base pairs with low contact order (the number of nucleotides that separate the paired nucleotides), and when chemical modification information is used to constrain folding (11,12). Accuracies tend to be poor for longer RNAs, and there are important short RNAs for which the prediction sensitivity is zero (12, 13). Results Structure of Escherichia coli 16S...

Conspectus Riboswitches, which were discovered in the first years of the XXI century, are gene-regulatory mRNA domains that respond to the intracellular concentration of a variety of metabolites and second messengers. They control essential genes in many pathogenic bacteria, and represent a new class of biomolecular target for the development of antibiotics and chemical-biological tools. Five mechanisms of gene regulation are known for riboswitches. Most bacterial riboswitches modulate transcription termination or translation initiation in response to ligand binding. All known examples of eukaryotic riboswitches and some bacterial riboswitches control gene expression by alternative splicing. The glmS riboswitch, widespread in Gram-positive bacteria, is a catalytic RNA activated by ligand binding. Its self-cleavage destabilizes the mRNA of which it is part. Finally, one example of trans-acting riboswitch is known. Three-dimensional (3D) structures have been determined of representatives of thirteen structurally distinct riboswitch classes, providing atomic-level insight into their mechanisms of ligand recognition. While cellular and viral RNAs in general have attracted interest as potential drug targets, riboswitches show special promise due to the diversity and sophistication of small molecule recognition strategies on display in their ligand binding pockets. Moreover, uniquely among known structured RNA domains, riboswitches evolved to recognize small molecule ligands. Structural and biochemical advances in the study of riboswitches provide an impetus for the development of methods for the discovery of novel riboswitch activators and inhibitors. Recent rational drug design efforts focused on select riboswitch classes have yielded a small number of candidate antibiotic compounds, including one active in a mouse model of Staphylococcus aureus infection. The development of high-throughput methods suitable for riboswitch-specific drug discovery is ongoing. A fragment-based screening approach employing equilibrium dialysis that may be generically useful has had early success. Riboswitch-mediated gene regulation is widely employed by bacteria; however, only the thiamine pyrophosphate-responsive riboswitch has thus far been found in eukaryotes. Thus, riboswitches are particularly attractive as targets for antibacterials. Indeed, antimicrobials with previously unknown mechanisms have been found to function by binding riboswitches and leading to aberrant gene expression.

Hydroxyl-selective electrophiles, including N-methylisatoic anhydride (NMIA) and 1-methyl-7-nitroisatoic anhydride (1M7), are broadly useful for RNA structure analysis because they react preferentially with the ribose 29-OH group at conformationally unconstrained or flexible nucleotides. Each nucleotide in an RNA has the potential to form an adduct with these reagents to yield a comprehensive, nucleotide-resolution, view of RNA structure. However, it is possible that factors other than local structure modulate reactivity. To evaluate the influence of base identity on the intrinsic reactivity of each nucleotide, we analyze NMIA and 1M7 reactivity using four distinct RNAs, under both native and denaturing conditions. We show that guanosine and adenosine residues have identical intrinsic 29-hydroxyl reactivities at pH 8.0 and are 1.4 and 1.7 times more reactive than uridine and cytidine, respectively. These subtle, but statistically significant, differences do not impact the ability of selective 29-hydroxyl acylation analyzed by primer extension-based (SHAPE) methods to establish an RNA secondary structure or monitor RNA folding in solution because base-specific influences are much smaller than the reactivity differences between paired and unpaired nucleotides.