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/.

Adeno-associated virus (AAV)-mediated CRISPR-Cas9 editing holds promise to treat many diseases. The immune response to bacterial-derived Cas9 has been speculated as a hurdle for AAV-CRISPR therapy. However, immunological consequences of AAV-mediated Cas9 expression have thus far not been thoroughly investigated in large mammals. We evaluate Cas9-specific immune responses in canine models of Duchenne muscular dystrophy (DMD) following intramuscular and intravenous AAV-CRISPR therapy. Treatment results initially in robust dystrophin restoration in affected dogs but also induces muscle inflammation, and Cas9-specific humoral and cytotoxic T-lymphocyte (CTL) responses that are not prevented by the muscle-specific promoter and transient prednisolone immune suppression. In normal dogs, AAV-mediated Cas9 expression induces similar, though milder, immune responses. In contrast, other therapeutic (micro-dystrophin and SERCA2a) and reporter (alkaline phosphatase, AP) vectors result in persistent expression without inducing muscle inflammation. Our results suggest Cas9 immunity may represent a critical barrier for AAV-CRISPR therapy in large mammals.

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion–RNA and ligand–RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data–directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

While CRISPR/Cas9 is a powerful tool in genome engineering, the on-target activity and off-target effects of the system widely vary because of the differences in guide RNA (gRNA) sequences and genomic environments. Traditional approaches rely on separate models and parameters to treat on- and off-target cleavage activities. Here, we demonstrate that a free-energy scheme dominates the Cas9 editing efficacy and delineate a method that simultaneously considers on-target activities and off-target effects. While data-driven machine-learning approaches learn rules to model particular datasets, they may not be as transferrable to new systems or capable of producing new mechanistic insights as principled physical approaches. By integrating the energetics of R-loop formation under Cas9 binding, the effect of the protospacer adjacent motif sequence, and the folding stability of the whole single guide RNA, we devised a unified, physical model that can apply to any cleavage-activity dataset. This unified framework improves predictions for both on-target activities and off-target efficiencies of spCas9 and may be readily transferred to other systems with different guide RNAs or Cas9 ortholog proteins.