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 chemicalmapping 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/.

BackgroundComputational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. The goal of the refinement is not only to improve the local quality of the model but to bring it globally closer to the true structure.ResultsWe present QRNAS, a software tool for fine-grained refinement of nucleic acid structures, which is an extension of the AMBER simulation method with additional restraints. QRNAS is capable of handling RNA, DNA, chimeras, and hybrids thereof, and enables modeling of nucleic acids containing modified residues.ConclusionsWe demonstrate the ability of QRNAS to improve the quality of models generated with different methods. QRNAS was able to improve MolProbity scores of NMR structures, as well as of computational models generated in the course of the RNA-Puzzles experiment. The overall geometry improvement may be associated with increased model accuracy, especially on the level of correctly modeled base-pairs, but the systematic improvement of root mean square deviation to the reference structure should not be expected. The method has been integrated into a computational modeling workflow, enabling improved RNA 3D structure prediction.

We demonstrate that DFT calculations performed with the local density approximation (LDA) allow for significantly better reproduction of lattice constants, the unit cell volume and the density of Ag(II)SO(4) than those done with generalized gradient approximation (GGA). The LDA+U scheme, which accounts for electronic correlation effects, enables the accurate prediction of the magnetic superexchange constant of this strongly correlated material and its band gap at the Fermi level. The character of the band gap places the compound on the borderline between a Mott insulator and a charge transfer insulator. The size of the band gap (0.82 eV) indicates that AgSO(4) is a ferrimagnetic semiconductor, and possibly an attractive material for spintronics. A bulk modulus of 27.0 GPa and a compressibility of 0.037 GPa(-1) were determined for AgSO(4) from the third-order Birch-Murnaghan isothermal equation of state up to 20 GPa. Several polymorphic types compete with the ambient pressure P-1 phase as the external pressure is increased. The P-1 phase is predicted to resist pressure-induced metallization up to at least 20 GPa.