In Crystallo Selection to Establish New RNA Crystal Contacts

Structure-based design of antisense oligonucleotides that inhibit SARS-CoV-2 replication

Antisense oligonucleotides (ASOs) are an emerging class of drugs that target RNAs. Current ASO designs strictly follow the rule of Watson-Crick base pairing along target sequences. However, RNAs often fold into structures that interfere with ASO hybridization. Here we developed a structure-based ASO design method and applied it to target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our method makes sure that ASO binding is compatible with target structures in three-dimensional (3D) space by employing structural design templates. These 3D-ASOs recognize the shapes and hydrogen bonding patterns of targets via tertiary interactions, achieving enhanced affinity and specificity. We designed 3D-ASOs that bind to the frameshift stimulation element and transcription regulatory sequence of SARS-CoV-2 and identified lead ASOs that strongly inhibit viral replication in human cells. We further optimized the lead sequences and characterized structure-activity relationship. The 3D-ASO technology helps fight coronavirus disease-2019 and is broadly applicable to ASO drug development.

Engineering Crystal Packing in RNA Structures I: Past and Future Strategies for Engineering RNA Packing in Crystals

X-ray crystallography remains a powerful method to gain atomistic insights into the catalytic and regulatory functions of RNA molecules. However, the technique requires the preparation of diffraction-quality crystals. This is often a resource- and time-consuming venture because RNA crystallization is hindered by the conformational heterogeneity of RNA, as well as the limited opportunities for stereospecific intermolecular interactions between RNA molecules. The limited success at crystallization explains in part the smaller number of RNA-only structures in the Protein Data Bank. Several approaches have been developed to aid the formation of well-ordered RNA crystals. The majority of these are construct-engineering techniques that aim to introduce crystal contacts to favor the formation of well-diffracting crystals. A typical example is the insertion of tetraloop-tetraloop receptor pairs into non-essential RNA segments to promote intermolecular association. Other methods of promoting crystallization involve chaperones and crystallization-friendly molecules that increase RNA stability and improve crystal packing. In this review, we discuss the various techniques that have been successfully used to facilitate crystal packing of RNA molecules, recent advances in construct engineering, and directions for future research in this vital aspect of RNA crystallography.

Cryo-EM structures of full-length Tetrahymena ribozyme at 3.1 Å resolution

Single-particle cryogenic electron microscopy (cryo-EM) has become a standard technique for determining protein structures at atomic resolution^1-3. However, cryo-EM studies of protein-free RNA are in their early days. The Tetrahymena thermophila group I self-splicing intron was the first ribozyme to be discovered and has been a prominent model system for the study of RNA catalysis and structure-function relationships⁴, but its full structure remains unknown. Here we report cryo-EM structures of the full-length Tetrahymena ribozyme in substrate-free and bound states at a resolution of 3.1 Å. Newly resolved peripheral regions form two coaxially stacked helices; these are interconnected by two kissing loop pseudoknots that wrap around the catalytic core and include two previously unforeseen (to our knowledge) tertiary interactions. The global architecture is nearly identical in both states; only the internal guide sequence and guanosine binding site undergo a large conformational change and a localized shift, respectively, upon binding of RNA substrates. These results provide a long-sought structural view of a paradigmatic RNA enzyme and signal a new era for the cryo-EM-based study of structure-function relationships in ribozymes.

Unboxing the T-box riboswitches-A glimpse into multivalent and multimodal RNA-RNA interactions

The T-box riboswitches are widespread bacterial noncoding RNAs that directly bind specific tRNAs, sense aminoacylation on bound tRNAs, and switch conformations to control amino-acid metabolism and to maintain nutritional homeostasis. The core mechanisms of tRNA recognition, amino acid sensing, and conformational switching by the T-boxes have been recently elucidated, providing a wealth of new insights into multivalent and multimodal RNA-RNA interactions. This review dissects the structures and tRNA-recognition mechanisms by the Stem I, Stem II, and Discriminator domains, which collectively compose the T-box riboswitches. It further compares and contrasts the two classes of T-boxes that regulate transcription and translation, respectively, and integrates recent findings to derive general themes, trends, and insights into complex RNA-RNA interactions. Specifically, the T-box paradigm reveals that noncoding RNAs can interact with each other through multiple coordinated contacts, concatenation of stacked helices, and mutually induced fit. Numerous tertiary contacts, especially those emanating from strings of single-stranded purines, act in concert to reinforce long-range base-pairing and stacking interactions. These coordinated, mixed-mode contacts allow the T-box RNA to sterically sense aminoacylation on the tRNA using a bipartite steric sieve, and to couple this readout to a conformational switch mediated by tRNA-T-box stacking. Together, the insights gleaned from the T-box riboswitches inform investigations into other complex RNA structures and assemblies, development of T-box-targeted antimicrobials, and may inspire design and engineering of novel RNA sensors, regulators, and interfaces. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs Regulatory RNAs/RNAi/Riboswitches > Riboswitches.

Selecting New RNA Crystal Contacts

RNAs are relatively difficult to crystallize because many sequence variants must be tested to obtain suitable crystal contacts. In this issue of Structure, Shoffner et al. (2018) report an in crystallo selection procedure that allows for the rapid generation of new RNA crystal contacts.

Computational modeling of RNA 3D structure based on experimental data

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.