A critical base pair in k-turns that confers folding characteristics and correlates with biological function

Kink turns (k-turns) are widespread elements in RNA that mediate tertiary contacts by kinking the helical axis. We have found that the ability of k-turns to undergo ion-induced folding is conferred by a single base pair that follows the conserved A·G pairs, that is, the 3b·3n position. A Watson–Crick pair leads to an inability to fold in metal ions alone, while 3n=G or 3b=C (but not both) permits folding. Crystallographic study reveals two hydrated metal ions coordinated to O6 of G3n and G2n of Kt-7. Removal of either atom impairs Mg(2+)-induced folding in solution. While SAM-I riboswitches have 3b·3n sequences that would predispose them to ion-induced folding, U4 snRNA are strongly biased to an inability to such folding. Thus riboswitch sequences allow folding to occur independently of protein binding, while U4 should remain unfolded until bound by protein. The empirical rules deduced for k-turn folding have strong predictive value

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures.:

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/

Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Modules Identification

International audienceRNA structures possess multiple levels of structural organization. Secondary structures are made of canonical (i.e. Watson-Crick and Wobble) helices, connected by loops whose local conformations are critical determinants of global 3D architectures. Such local 3D structures consist of conserved sets of non-canonical base pairs, called RNA modules. Their prediction from sequence data is thus a milestone toward 3D structure modelling. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in modules databases. Here, we introduce BayesPairing 2, a new sequence search algorithm leveraging secondary structure tree decomposition which allows to reduce the computational complexity and improve predictions on new sequences. We benchmarked our methods on 75 modules and 6360 RNA sequences, and report accuracies that are comparable to the state of the art, with considerable running time improvements. When identifying 200 modules on a single sequence, BayesPairing 2 is over 100 times faster than its previous version, opening new doors for genome-wide applications