SARS-CoV-2 is a betacoronavirus with a linear single-stranded, positive-sense RNA genome, whose outbreak caused the ongoing COVID-19 pandemic. The ability of coronaviruses to rapidly evolve, adapt, and cross species barriers makes the development of effective and durable therapeutic strategies a challenging and urgent need. As for other RNA viruses, genomic RNA structures are expected to play crucial roles in several steps of the coronavirus replication cycle. Despite this, only a handful of functionally-conserved coronavirus structural RNA elements have been identified to date. Here, we performed RNA structure probing to obtain single-base resolution secondary structure maps of the full SARS-CoV-2 coronavirus genome both in vitro and in living infected cells. Probing data recapitulate the previously described coronavirus RNA elements (5′ UTR and s2m), and reveal new structures. Of these, ∼10.2% show significant covariation among SARS-CoV-2 and other coronaviruses, hinting at their functionally-conserved role. Secondary structure-restrained 3D modeling of these segments further allowed for the identification of putative druggable pockets. In addition, we identify a set of single-stranded segments in vivo, showing high sequence conservation, suitable for the development of antisense oligonucleotide therapeutics. Collectively, our work lays the foundation for the development of innovative RNA-targeted therapeutic strategies to fight SARS-related infections.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson–Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.

Computational 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. We 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. We 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.

Correct identification and effective visualization of interactions in biomolecular structures facilitate understanding of their functions and molecular design. In response to the practical needs of structure-based analysis, we have created a Mapiya web server. The Mapiya integrates four main functionalities: (i) generation of contact maps – intramolecular and intermolecular—for proteins, nucleic acids, and their complexes; (ii) characterization of the interactions physicochemical nature, (iii) interactive visualization of biomolecular conformations with automatic zoom on selected contacts using Molstar and (iv) additional sequence- and structure-based analyses performed with third-party software and in-house algorithms combined into an easy-to-use interface. Thus, Mapiya offers a highly customized analysis of the molecular interactions' in various biological systems. The web server is available at: http://mapiya.lcbio.pl/

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein–RNA interfaces to probe the binding hot spots at protein–RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein–protein and protein–RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein–RNA recognition sites with desired affinity.

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.