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Alternative splicing is a highly sophisticated process, playing a significant role in posttranscriptional gene expression and underlying the diversity and complexity of organisms. Its regulation is multilayered, including an intrinsic role of RNA structural arrangement which undergoes time- and tissue-specific alterations. In this review, we describe the principles of RNA structural arrangement and briefly decipher its cis- and trans-acting cellular modulators which serve as crucial determinants of biological functionality of the RNA structure. Subsequently, we engage in a discussion about the RNA structure-mediated mechanisms of alternative splicing regulation. On one hand, the impairment of formation of optimal RNA structures may have critical consequences for the splicing outcome and further contribute to understanding the pathomechanism of severe disorders. On the other hand, the structural aspects of RNA became significant features taken into consideration in the endeavor of finding potential therapeutic treatments. Both aspects have been addressed by us emphasizing the importance of ongoing studies in both fields.

Ribonucleic acids (RNAs) fold into complex structures that are strongly associated with their biological functions. These can be abstracted into secondary structures, represented as nucleotide sequences annotated with base-pairing information. This abstraction is both biologically relevant and computationally manageable. Comparing and classifying RNA molecules typically relies on these secondary structure representations, which exist in multiple formats. In this work, we introduce TARNAS 1.0, a software tool designed to convert RNA secondary structure representations across multiple formats, including Base Pair Sequence (BPSEQ), Connect Table (CT), dot-bracket, Arc-Annotated Sequence (AAS), Fast-All (FASTA), and RNA Markup Language (RNAML). The tool offers options for retaining or removing comments, blank lines, and headers during the conversion process. These format translation and preprocessing capabilities are specifically designed to support the batch handling of large collections of RNA molecules, making TARNAS well suited for large dataset construction and database curation. Beyond format translation, TARNAS computes three levels of abstraction for RNA secondary structures, namely core, core plus, and shape, as well as a set of statistical descriptors for both primary and secondary structure. These abstraction and analysis features are intended to facilitate the comparison of molecules and the identification of recurring structural patterns, which are essential steps for associating structural motifs with molecular function. TARNAS is available as both a standalone desktop application and a web-based tool. The desktop version supports batch processing of large datasets, while the web version is optimized for the analysis of single molecules.

To generate infectious virus, HIV-1 must package two copies of its full-length RNA into particles. HIV-1 transcription initiates from multiple, neighboring sites, generating RNA species that only differ by a few nucleotides at the 5′ end, including those with one (1G) or three (3G) 5′ guanosines. Strikingly, 1G RNA is preferentially packaged into virions over 3G RNA. We investigated how HIV-1 distinguishes between these nearly identical RNAs using in-gel chemical probing combined with recently developed computational tools for determining RNA conformational ensembles, as well as cell-based assays to quantify the efficiency of RNA packaging into viral particles. We found that 1G and 3G RNAs fold into distinct structural ensembles. The 1G RNA, but not the 3G RNA, primarily adopts conformations with an intact polyA stem, exposed dimerization initiation site, and multiple, unpaired guanosines known to mediate Gag binding. Furthermore, we identified mutants that exhibited altered genome selectivity and packaged 3G RNA efficiently. In these mutants, both 1G and 3G RNAs fold into similar conformational ensembles, such that they can no longer be distinguished. Our findings demonstrate that polyA stem stability guides RNA-packaging selectivity. These studies also uncover the mechanism by which HIV-1 selects its genome for packaging: 1G RNA is preferentially packaged because it exposes structural elements that promote RNA dimerization and Gag binding.

The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.

As nascent polypeptide chains are synthesized, they pass through a tunnel in the large ribosomal subunit. Interaction between specific nascent chains and the ribosomal tunnel is used to induce translational stalling for the regulation of gene expression. One well-characterized example is the Escherichia coli SecM (secretion monitor) gene product, which induces stalling to up-regulate translation initiation of the downstream secA gene, which is needed for protein export. Although many of the key components of SecM and the ribosomal tunnel have been identified, understanding of the mechanism by which the peptidyl transferase center of the ribosome is inactivated has been lacking. Here we present a cryo-electron microscopy reconstruction of a SecM-stalled ribosome nascent chain complex at 5.6 Å. While no cascade of rRNA conformational changes is evident, this structure reveals the direct interaction between critical residues of SecM and the ribosomal tunnel. Moreover, a shift in the position of the tRNA-nascent peptide linkage of the SecM-tRNA provides a rationale for peptidyl transferase center silencing, conditional on the simultaneous presence of a Pro-tRNA(Pro) in the ribosomal A-site. These results suggest a distinct allosteric mechanism of regulating translational elongation by the SecM stalling peptide.

Background Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm. Results In this paper, we present the updated version of the SETTER web server that implements a user friendly interface to the MultiSETTER algorithm. The server accepts RNA structures either as the list of PDB IDs or as user-defined PDB files. After the superposition is computed, structures are visualized in 3D and several reports and statistics are generated. Conclusion To the best of our knowledge, the MultiSETTER web server is the first publicly available tool for a multiple RNA structure alignment. The MultiSETTER server offers the visual inspection of an alignment in 3D space which may reveal structural and functional relationships not captured by other multiple alignment methods based either on a sequence or on secondary structure motifs.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present patteRNA , an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that patteRNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. patteRNA is versatile and compatible with diverse profiling techniques and experimental conditions.

Since the outbreak of severe acute respiratory syndrome (SARS) in 2003, the three-dimensional structures of several of the replicase/transcriptase components of SARS coronavirus (SARS-CoV), the non-structural proteins (Nsps), have been determined. However, within the large Nsp3 (1922 amino-acid residues), the structure and function of the so-called SARS-unique domain (SUD) have remained elusive. SUD occurs only in SARS-CoV and the highly related viruses found in certain bats, but is absent from all other coronaviruses. Therefore, it has been speculated that it may be involved in the extreme pathogenicity of SARS-CoV, compared to other coronaviruses, most of which cause only mild infections in humans. In order to help elucidate the function of the SUD, we have determined crystal structures of fragment 389-652 (\"SUD(core)\") of Nsp3, which comprises 264 of the 338 residues of the domain. Both the monoclinic and triclinic crystal forms (2.2 and 2.8 A resolution, respectively) revealed that SUD(core) forms a homodimer. Each monomer consists of two subdomains, SUD-N and SUD-M, with a macrodomain fold similar to the SARS-CoV X-domain. However, in contrast to the latter, SUD fails to bind ADP-ribose, as determined by zone-interference gel electrophoresis. Instead, the entire SUD(core) as well as its individual subdomains interact with oligonucleotides known to form G-quadruplexes. This includes oligodeoxy- as well as oligoribonucleotides. Mutations of selected lysine residues on the surface of the SUD-N subdomain lead to reduction of G-quadruplex binding, whereas mutations in the SUD-M subdomain abolish it. As there is no evidence for Nsp3 entering the nucleus of the host cell, the SARS-CoV genomic RNA or host-cell mRNA containing long G-stretches may be targets of SUD. The SARS-CoV genome is devoid of G-stretches longer than 5-6 nucleotides, but more extended G-stretches are found in the 3'-nontranslated regions of mRNAs coding for certain host-cell proteins involved in apoptosis or signal transduction, and have been shown to bind to SUD in vitro. Therefore, SUD may be involved in controlling the host cell's response to the viral infection. Possible interference with poly(ADP-ribose) polymerase-like domains is also discussed.

The increasing number and complexity of structures containing RNA chains in the Protein Data Bank (PDB) have led to the need for automated structure annotation methods to replace or complement expert visual curation. This is especially true when searching for tertiary base motifs and substructures. Such base arrangements and motifs have diverse roles that range from contributions to structural stability to more direct involvement in the molecule’s functions, such as the sites for ligand binding and catalytic activity. We review the utility of computational approaches in annotating RNA tertiary base motifs in a dataset of PDB structures, particularly the use of graph theoretical algorithms that can search for such base motifs and annotate them or find and annotate clusters of hydrogen-bond-connected bases. We also demonstrate how such graph theoretical algorithms can be integrated into a workflow that allows for functional analysis and comparisons of base arrangements and sub-structures, such as those involved in ligand binding. The capacity to carry out such automatic curations has led to the discovery of novel motifs and can give new context to known motifs as well as enable the rapid compilation of RNA 3D motifs into a database.