RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

RNA structure inference through chemical mapping after accidental or intentional mutations.

Despite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical-mapping method called "mutate-and-map read out through next-generation sequencing" (M2-seq) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double-DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a neural-network-inspired algorithm called M2-net. Measurements on the P4-P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

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/.

Publisher Correction: Controllable molecular motors engineered from myosin and RNA.

An incorrect Supplementary Information file was originally published. The file has been replaced with the correct one.

Controllable molecular motors engineered from myosin and RNA.

Engineering biomolecular motors can provide direct tests of structure-function relationships and customized components for controlling molecular transport in artificial systems 1 or in living cells 2 . Previously, synthetic nucleic acid motors 3-5 and modified natural protein motors 6-10 have been developed in separate complementary strategies to achieve tunable and controllable motor function. Integrating protein and nucleic-acid components to form engineered nucleoprotein motors may enable additional sophisticated functionalities. However, this potential has only begun to be explored in pioneering work harnessing DNA scaffolds to dictate the spacing, number and composition of tethered protein motors 11-15 . Here, we describe myosin motors that incorporate RNA lever arms, forming hybrid assemblies in which conformational changes in the protein motor domain are amplified and redirected by nucleic acid structures. The RNA lever arm geometry determines the speed and direction of motor transport and can be dynamically controlled using programmed transitions in the lever arm structure 7,9 . We have characterized the hybrid motors using in vitro motility assays, single-molecule tracking, cryo-electron microscopy and structural probing 16 . Our designs include nucleoprotein motors that reversibly change direction in response to oligonucleotides that drive strand-displacement 17 reactions. In multimeric assemblies, the controllable motors walk processively along actin filaments at speeds of 10-20 nm s-1. Finally, to illustrate the potential for multiplexed addressable control, we demonstrate sequence-specific responses of RNA variants to oligonucleotide signals.

Modeling complex RNA tertiary folds with Rosetta.

Reliable modeling of RNA tertiary structures is key to both understanding these structures' roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100-300 nucleotide size range at consistent subhelical (~1 nm) resolution. Our laboratory's current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.

Consistent global structures of complex RNA states through multidimensional chemical mapping.

Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OH Cleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.