We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA-small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA-ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a "meta-predictor" leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http:// ligandrna.genesilico.pl.

Splicing aberrations induced as a consequence of the sequestration of muscleblind-like splicing factors on the dystrophia myotonica protein kinase transcript, which contains expanded CUG repeats, present a major pathomechanism of myotonic dystrophy type 1 (DM1). As muscleblind-like factors may also be important factors involved in the biogenesis of circular RNAs (circRNAs), we hypothesized that the level of circRNAs would be decreased in DM1. To test this hypothesis, we selected 20 well-validated circRNAs and analyzed their levels in several experimental systems (e.g., cell lines, DM muscle tissues, and a mouse model of DM1) using droplet digital PCR assays. We also explored the global level of circRNAs using two RNA-Seq datasets of DM1 muscle samples. Contrary to our original hypothesis, our results consistently showed a global increase in circRNA levels in DM1, and we identified numerous circRNAs that were increased in DM1. We also identified many genes (including muscle-specific genes) giving rise to numerous (>10) circRNAs. Thus, this study is the first to show an increase in global circRNA levels in DM1. We also provided preliminary results showing the association of circRNA level with muscle weakness and alternative splicing changes that are biomarkers of DM1 severity.

Motivation: Metal ions are essential for the folding of RNA molecules into stable tertiary structures and are often involved in the catalytic activity of ribozymes. However, the positions of metal ions in RNA 3D structures are difficult to determine experimentally. This motivated us to develop a computational predictor of metal ion sites for RNA structures.Results: We developed a statistical potential for predicting positions of metal ions (magnesium, sodium and potassium), based on the analysis of binding sites in experimentally solved RNA structures. The MetalionRNA program is available as a web server that predicts metal ions for RNA structures submitted by the user.Availability: The MetalionRNA web server is accessible at http://metalionrna.genesilico.pl/.Contact: iamb@genesilico.plSupplementary information: Supplementary data are available at Bioinformatics online.