A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in wellfolded RNAs were identified. Information is encoded in an RNA molecule at two levels: in its primary sequence and in its ability to form higher-order secondary and tertiary structures. Nearly all RNAs can fold to form some secondary structure and, in many RNAs, highly structured regions encode important regulatory motifs. Such structured regulatory elements can be composed of canonical base pairs but may also feature specialized and distinctive RNA structures. Among the best characterized of these specialized structures are RNA pseudoknots. Pseudoknots are relatively rare but occur overwhelmingly in functionally important regions of RNA (2-4). For example, all of the large catalytic RNAs contain pseudoknots (5, 6); roughly two-thirds of the known classes of riboswitches contain pseudoknots that appear to be essential for ligand binding and gene regulatory functions (7); and pseudoknots occur prominently in the regulatory elements that viruses use to usurp cellular metabolism (3). Pseudoknots are thus harbingers of biological function. An important and challenging goal is to identify these structures reliably.Pseudoknots are excluded from the most widely used algorithms that model RNA secondary structure (8). This exclusion is based on the challenge of incorporating the pseudoknot structure into the efficient dynamic programming algorithm used in the most popular secondary structure prediction approaches and because of the additional computational effort required. The prediction of lowest free energy structures with pseudoknots is NP-complete (9), which means that lowest free energy structure cannot be solved as a function of sequence length in polynomial time. In addition, allowing pseudoknots greatly increases the number of (incorrect) hel...

RNAstructure is a software package for RNA secondary structure prediction and analysis. This contribution describes a new set of web servers to provide its functionality. The web server offers RNA secondary structure prediction, including free energy minimization, maximum expected accuracy structure prediction and pseudoknot prediction. Bimolecular secondary structure prediction is also provided. Additionally, the server can predict secondary structures conserved in either two homologs or more than two homologs. Folding free energy changes can be predicted for a given RNA structure using nearest neighbor rules. Secondary structures can be compared using circular plots or the scoring methods, sensitivity and positive predictive value. Additionally, structure drawings can be rendered as SVG, postscript, jpeg or pdf. The web server is freely available for public use at: http://rna.urmc.rochester.edu/RNAstructureWeb.

It is a significant challenge to predict RNA secondary structures including pseudoknots. Here, a new algorithm capable of predicting pseudoknots of any topology, ProbKnot, is reported. ProbKnot assembles maximum expected accuracy structures from computed base-pairing probabilities in O(N 2 ) time, where N is the length of the sequence. The performance of ProbKnot was measured by comparing predicted structures with known structures for a large database of RNA sequences with fewer than 700 nucleotides. The percentage of known pairs correctly predicted was 69.3%. Additionally, the percentage of predicted pairs in the known structure was 61.3%. This performance is the highest of four tested algorithms that are capable of pseudoknot prediction. The program is available for download at: http://rna.urmc.rochester.edu/RNAstructure.html.

The 5′ and 3′ termini of RNA play important roles in many cellular processes. Using Förster resonance energy transfer (FRET), we show that mRNAs and lncRNAs have an intrinsic propensity to fold in the absence of proteins into structures in which the 5′ end and 3′ end are ≤7 nm apart irrespective of mRNA length. Computational estimates suggest that the inherent proximity of the ends is a universal property of most mRNA and lncRNA sequences. Only guanosine-depleted RNA sequences with low sequence complexity are unstructured and exhibit end-to-end distances expected for the random coil conformation of RNA. While the biological implications remain to be explored, short end-to-end distances could facilitate the binding of protein factors that regulate translation initiation by bridging mRNA 5′ and 3′ ends. Furthermore, our studies provide the basis for measuring, computing and manipulating end-to-end distances and secondary structure in RNA in research and biotechnology.

Intron detention in precursor RNAs serves to regulate expression of a substantial fraction of genes in eukaryotic genomes. How detained intron (DI) splicing is controlled is poorly understood. Here, we show that a ubiquitous post-translational modification called O-GlcNAc, which is thought to integrate signaling pathways as nutrient conditions fluctuate, controls detained intron splicing. Using specific inhibitors of the enzyme that installs O-GlcNAc (O-GlcNAc transferase, or OGT) and the enzyme that removes O-GlcNAc (O-GlcNAcase, or OGA), we first show that O-GlcNAc regulates splicing of the highly conserved detained introns in OGT and OGA to control mRNA abundance in order to buffer O-GlcNAc changes. We show that OGT and OGA represent two distinct paradigms for how DI splicing can control gene expression. We also show that when DI splicing of the O-GlcNAc-cycling genes fails to restore O-GlcNAc homeostasis, there is a global change in detained intron levels. Strikingly, almost all detained introns are spliced more efficiently when O-GlcNAc levels are low, yet other alternative splicing pathways change minimally. Our results demonstrate that O-GlcNAc controls detained intron splicing to tune system-wide gene expression, providing a means to couple nutrient conditions to the cell's transcriptional regime.