FR3D: finding local and composite recurrent structural motifs in RNA 3D structures

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.

High-resolution RNA tertiary structures in Zika virus stem-loop A for the development of inhibitory small molecules

Flaviviruses such as Zika (ZIKV) and dengue virus (DENV) are positive-sense RNA viruses belonging to Flaviviridae The flavivirus genome contains a 5' end stem-loop promoter sequence known as stem-loop A (SLA) that is recognized by the flavivirus polymerase NS5 during viral RNA synthesis and 5' guanosine cap methylation. The crystal structures of ZIKV and DENV SLAs show a well-defined fold, consisting of a bottom stem, side loop, and top stem-loop, providing unique interaction sites for small molecule inhibitors to disrupt the promoter function. To facilitate the identification of small molecule binding sites in flavivirus SLA, we determined high-resolution structures of the bottom and top stems of ZIKV SLA, which contain a single U- or G-bulge, respectively. Both bulge nucleotides exhibit multiple orientations, from folded back on the adjacent nucleotide to flipped out of the helix, and are stabilized by stacking or base triple interactions. These structures suggest that even a single unpaired nucleotide can provide flexibility to RNA structures, and its conformation is mainly determined by the stabilizing chemical environment. To facilitate discovery of small molecule inhibitors that interfere with the functions of ZIKV SLA, we screened and identified compounds that bind to the bottom and top stems of ZIKV SLA.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

When will RNA get its AlphaFold moment?

The protein structure prediction problem has been solved for many types of proteins by AlphaFold. Recently, there has been considerable excitement to build off the success of AlphaFold and predict the 3D structures of RNAs. RNA prediction methods use a variety of techniques, from physics-based to machine learning approaches. We believe that there are challenges preventing the successful development of deep learning-based methods like AlphaFold for RNA in the short term. Broadly speaking, the challenges are the limited number of structures and alignments making data-hungry deep learning methods unlikely to succeed. Additionally, there are several issues with the existing structure and sequence data, as they are often of insufficient quality, highly biased and missing key information. Here, we discuss these challenges in detail and suggest some steps to remedy the situation. We believe that it is possible to create an accurate RNA structure prediction method, but it will require solving several data quality and volume issues, usage of data beyond simple sequence alignments, or the development of new less data-hungry machine learning methods.

A comprehensive survey of long-range tertiary interactions and motifs in non-coding RNA structures

Understanding the 3D structure of RNA is key to understanding RNA function. RNA 3D structure is modular and can be seen as a composition of building blocks of various sizes called tertiary motifs. Currently, long-range motifs formed between distant loops and helical regions are largely less studied than the local motifs determined by the RNA secondary structure. We surveyed long-range tertiary interactions and motifs in a non-redundant set of non-coding RNA 3D structures. A new dataset of annotated LOng-RAnge RNA 3D modules (LORA) was built using an approach that does not rely on the automatic annotations of non-canonical interactions. An original algorithm, ARTEM, was developed for annotation-, sequence- and topology-independent superposition of two arbitrary RNA 3D modules. The proposed methods allowed us to identify and describe the most common long-range RNA tertiary motifs. Along with the prevalent canonical A-minor interactions, a large number of previously undescribed staple interactions were observed. The most frequent long-range motifs were found to belong to three main motif families: planar staples, tilted staples, and helical packing motifs.

WVDL: Weighted Voting Deep Learning Model for Predicting RNA-Protein Binding Sites

RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-protein binding sites. Using weighted voting method to integrate multiple basic classifier models can improve model performance. Thus, in our study, we propose a weighted voting deep learning model (WVDL), which uses weighted voting method to combine convolutional neural network (CNN), long short term memory network (LSTM) and residual network (ResNet). First, the final forecast result of WVDL outperforms the basic classifier models and other ensemble strategies. Second, WVDL can extract more effective features by using weighted voting to find the best weighted combination. And, the CNN model also can draw the predicted motif pictures. Third, WVDL gets a competitive experiment result on public RBP-24 datasets comparing with other state-of-the-art methods. The source code of our proposed WVDL can be found in https://github.com/biomg/WVDL.

RNAMotifComp: a comprehensive method to analyze and identify structurally similar RNA motif families

MOTIVATION

The 3D structures of RNA play a critical role in understanding their functionalities. There exist several computational methods to study RNA 3D structures by identifying structural motifs and categorizing them into several motif families based on their structures. Although the number of such motif families is not limited, a few of them are well-studied. Out of these structural motif families, there exist several families that are visually similar or very close in structure, even with different base interactions. Alternatively, some motif families share a set of base interactions but maintain variation in their 3D formations. These similarities among different motif families, if known, can provide a better insight into the RNA 3D structural motifs as well as their characteristic functions in cell biology.

RESULTS

In this work, we proposed a method, RNAMotifComp, that analyzes the instances of well-known structural motif families and establishes a relational graph among them. We also have designed a method to visualize the relational graph where the families are shown as nodes and their similarity information is represented as edges. We validated our discovered correlations of the motif families using RNAMotifContrast. Additionally, we used a basic Naïve Bayes classifier to show the importance of RNAMotifComp. The relational analysis explains the functional analogies of divergent motif families and illustrates the situations where the motifs of disparate families are predicted to be of the same family.

AVAILABILITY AND IMPLEMENTATION

Source code publicly available at https://github.com/ucfcbb/RNAMotifFamilySimilarity.

RNA-NRD: a non-redundant RNA structural dataset for benchmarking and functional analysis

The significance of RNA functions and their role in evolution and disease control have remarkably increased the research scope in the field of RNA science. Though the availability of RNA structure data in PBD has been growing tremendously, maintaining their quality and integrity has become the greater challenge. Since the data available in PDB are results of different independent research, they might contain redundancy. As a result, there remains a possibility of data bias for both protein and RNA chains. Quite a few studies have been conducted to remove the redundancy of protein structures by introducing high-quality representatives. However, the amount of research done to remove the redundancy of RNA structures is still very low. To remove RNA chain redundancy in PDB, we have introduced RNA-NRD, a non-redundant dataset of RNA chains based on sequence and 3D structural similarity. We compared RNA-NRD with the existing non-redundant RNA structure dataset RS-RNA and showed that it has better-formed clusters of redundant RNA chains with lower average RMSD and higher average PSI, thus improving the overall quality of the dataset.

Dissecting the energetic architecture within an RNA tertiary structural motif via high-throughput thermodynamic measurements

Structured RNAs and RNA/protein complexes perform critical cellular functions. They often contain structurally conserved tertiary contact "motifs," whose occurrence simplifies the RNA folding landscape. Prior studies have focused on the conformational and energetic modularity of intact motifs. Here, we turn to the dissection of one common motif, the 11nt receptor (11ntR), using quantitative analysis of RNA on a massively parallel array to measure the binding of all single and double 11ntR mutants to GAAA and GUAA tetraloops, thereby probing the energetic architecture of the motif. While the 11ntR behaves as a motif, its cooperativity is not absolute. Instead, we uncovered a gradient from high cooperativity amongst base-paired and neighboring residues to additivity between distant residues. As expected, substitutions at residues in direct contact with the GAAA tetraloop resulted in the largest decreases to binding, and energetic penalties of mutations were substantially smaller for binding to the alternate GUAA tetraloop, which lacks tertiary contacts present with the canonical GAAA tetraloop. However, we found that the energetic consequences of base partner substitutions are not, in general, simply described by base pair type or isostericity. We also found exceptions to the previously established stability-abundance relationship for 11ntR sequence variants. These findings of "exceptions to the rule" highlight the power of systematic high-throughput approaches to uncover novel variants for future study in addition to providing an energetic map of a functional RNA.

Targeted nanomedicine: Lessons learned and future directions

Designing a therapeutic modality that will reach a certain organ, tissue, or cell type is crucial for both the therapeutic efficiency and to limit off-target adverse effects. Nanoparticles carrying various drugs, such as nucleic acids, small molecules and proteins, are promoting modalities to this end. Beyond the need to identify a target for a specific indication, an adequate design has to address the multiple biological barriers, such as systemic barriers, dilution and unspecific distribution, tissue penetration and intracellular trafficking. The field of targeted delivery has developed rapidly in recent years, with tremendous progress made in understating the biological barriers, and new technologies to functionalize nanoparticles with targeting moieties for an accurate, specific and highly selective delivery. Implementing new approaches like multi-functionalized nanocarriers and machine learning models will advance the field for designing safe, cell -specific nanoparticle delivery systems. Here, we will critically review the current progress in the field and suggest novel strategies to improve cell specific delivery of therapeutic payloads.

Seriation Using Tree-penalized Path Length

Given a sample of n data points and an n by n dissimilarity matrix, data seriation methods produce a linear ordering of the objects, putting similar objects nearby in the ordering. One may visualize the reordered dissimilarity matrix with a heat map and thus understand the structure of the data, while still displaying the full matrix of dissimilarities. Good orderings produce heat maps that are easy to read and allow for clear interpretation. We consider two popular seriation methods, minimizing path length by solving the Traveling Salesman Problem (TSP), and Optimal Leaf Ordering (OLO), which minimizes path length among all orderings consistent with a given tree structure. Learning from the strengths and weaknesses of the two methods, we introduce a new hybrid seriation method, tree-penalized Path Length (tpPL). The objective is a linear combination of path length and the extent of violations of the tree structure, with a parameter that transitions the optimal paths smoothly from TSP to OLO. We present a detailed study over 44 synthetic datasets which are designed to bring out the strengths and weaknesses of the three methods, finding that the hybrid nature of tpPL enables it to overcome the weaknesses of TSP and OLO.

Automated 3D Design and Evaluation of RNA Nanostructures with RNAMake

Despite growing interest in applying RNA's unique structural characteristics to solve diverse biotechnology and nanotechnology problems, there are few computational tools for targeted tertiary design. As a result, RNA 3D design is traditionally slow, resource-consuming, and dependent on expert modeling. In this chapter, we discuss our recently developed software package: RNAMake, a set of applications capable of designing RNA tertiary structures to solve various relevant nanotechnology problems and provide basic thermodynamic calculations for the generated designs. We provide in-depth examples and instructions for designing example RNA nanostructures such as minimal RNA sequences containing a single tertiary contact, generating RNAs that stabilize small-molecule ligands, and building tethers that link ribosomal subunits together. We also highlight the addition of a new Monte Carlo design algorithm and the ability to estimate the thermodynamic contribution of helical elements in RNA 3D structures.

Structural and Energetic Features of Base-Base Stacking Contacts in RNA

Nucleobase π-π stacking is one of the crucial organizing interactions within three-dimensional (3D) RNA architectures. Characterizing the structural variability of these contacts in RNA crystal structures will help delineate their subtleties and their role in determining function. This analysis of different stacking geometries found in RNA X-ray crystal structures is the largest such survey to date; coupled with quantum-mechanical calculations on typical representatives of each possible stacking arrangement, we determined the distribution of stacking interaction energies. A total of 1,735,481 stacking contacts, spanning 359 of the 384 theoretically possible distinct stacking geometries, were identified. Our analysis reveals preferential occurrences of specific consecutive stacking arrangements in certain regions of RNA architectures. Quantum chemical calculations suggest that 88 of the 359 contacts possess intrinsically stable stacking geometries, whereas the remaining stacks require the RNA backbone or surrounding macromolecular environment to force their formation and maintain their stability. Our systematic analysis of π-π stacks in RNA highlights trends in the occurrence and localization of these noncovalent interactions and may help better understand the structural intricacies of functional RNA-based molecular architectures.

Multiscale Modeling of Phosphate···π Contacts in RNA U-Turns Exposes Differences between Quantum-Chemical and AMBER Force Field Descriptions

Phosphate···π, also called anion···π, contacts occur between nucleobases and anionic phosphate oxygens (OP2) in r(GNRA) and r(UNNN) U-turn motifs (N = A,G,C,U; R = A,G). These contacts were investigated using state-of-the-art quantum-chemical methods (QM) to characterize their physicochemical properties and to serve as a reference to evaluate AMBER force field (AFF) performance. We found that phosphate···π interaction energies calculated with the AFF for dimethyl phosphate···nucleobase model systems are less stabilizing in comparison with double-hybrid DFT and that minimum contact distances are larger for all nucleobases. These distance stretches are also observed in large-scale AFF vs QM/MM computations and classical molecular dynamics (MD) simulations on several r(gcGNRAgc) tetraloop hairpins when compared to experimental data extracted from X-ray/cryo-EM structures (res. ≤ 2.5 Å) using the WebFR3D bioinformatic tool. MD simulations further revealed shifted OP2/nucleobase positions. We propose that discrepancies between the QM and AFF result from a combination of missing polarization in the AFF combined with too large AFF Lennard-Jones (LJ) radii of nucleobase carbon atoms in addition to an exaggerated short-range repulsion of the r^-12 LJ repulsive term. We compared these results with earlier data gathered on lone pair···π contacts in CpG Z-steps occurring in r(UNCG) tetraloops. In both instances, charge transfer calculations do not support any significant n → π* donation effects. We also investigated thiophosphate···π contacts that showed reduced stabilizing interaction energies when compared to phosphate···π contacts. Thus, we challenge suggestions that the experimentally observed enhanced thermodynamic stability of phosphorothioated r(GNRA) tetraloops can be explained by larger London dispersion.

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education

The archiving and dissemination of protein and nucleic acid structures as well as their structural, functional and biophysical annotations is an essential task that enables the broader scientific community to conduct impactful research in multiple fields of the life sciences. The Protein Data Bank in Europe (PDBe; pdbe.org) team develops and maintains several databases and web services to address this fundamental need. From data archiving as a member of the Worldwide PDB consortium (wwPDB; wwpdb.org), to the PDBe Knowledge Base (PDBe‐KB; pdbekb.org), we provide data, data‐access mechanisms, and visualizations that facilitate basic and applied research and education across the life sciences. Here, we provide an overview of the structural data and annotations that we integrate and make freely available. We describe the web services and data visualization tools we offer, and provide information on how to effectively use or even further develop them. Finally, we discuss the direction of our data services, and how we aim to tackle new challenges that arise from the recent, unprecedented advances in the field of structure determination and protein structure modeling.

A nuclear import pathway exploited by pathogenic noncoding RNAs

The prevailing view of intracellular RNA trafficking in eukaryotic cells is that RNAs transcribed in the nucleus either stay in the nucleus or cross the nuclear envelope, entering the cytoplasm for function. However, emerging evidence illustrates that numerous functional RNAs move in the reverse direction, from the cytoplasm to the nucleus. The mechanism underlying RNA nuclear import has not been well elucidated. Viroids are single-stranded circular noncoding RNAs that infect plants. Using Nicotiana benthamiana, tomato (Solanum lycopersicum), and nuclear-replicating viroids as a model, we showed that cellular IMPORTIN ALPHA-4 (IMPa-4) is likely involved in viroid RNA nuclear import, empirically supporting the involvement of Importin-based cellular pathway in RNA nuclear import. We also confirmed the involvement of a cellular protein (viroid RNA-binding protein 1 [VIRP1]) that binds both IMPa-4 and viroids. Moreover, a conserved C-loop in nuclear-replicating viroids serves as a key signal for nuclear import. Disrupting C-loop impairs VIRP1 binding, viroid nuclear accumulation, and infectivity. Further, C-loop exists in a subviral satellite noncoding RNA that relies on VIRP1 for nuclear import. These results advance our understanding of subviral RNA infection and the regulation of RNA nuclear import.

RNABPDB: Molecular Modeling of RNA Structure-From Base Pair Analysis in Crystals to Structure Prediction

The stable three-dimensional structure of RNA is known to play several important biochemical roles, from post-transcriptional gene regulation to enzymatic action. These structures contain double-helical regions, which often have different types of non-canonical base pairs in addition to Watson-Crick base pairs. Hence, it is important to study their structures from experimentally obtained or even predicted ones, to understand their role, or to develop a drug against the potential targets. Molecular Modeling of RNA double helices containing non-canonical base pairs is a difficult process, particularly due to the unavailability of structural features of non-Watson-Crick base pairs. Here we show a composite web-server with an associated database that allows one to generate the structure of RNA double helix containing non-canonical base pairs using consensus parameters obtained from the database. The database classification is followed by an evaluation of the central tendency of the structural parameters as well as a quantitative estimation of interaction strengths. These parameters are used to construct three-dimensional structures of double helices composed of Watson-Crick and/or non-canonical base pairs. Our benchmark study to regenerate double-helical fragments of many experimentally derived RNA structures indicate very high accuracy. This composite server is expected to be highly useful in understanding functions of various pre-miRNA by modeling structures of the molecules and estimating binding efficiency. The database can be accessed from http://hdrnas.saha.ac.in/rnabpdb .

Lone Pair…π Contacts and Structure Signatures of r(UNCG) Tetraloops, Z-Turns, and Z-Steps: A WebFR3D Survey

Z-DNA and Z-RNA have long appeared as oddities to nucleic acid scientists. However, their Z-step constituents are recurrently observed in all types of nucleic acid systems including ribosomes. Z-steps are NpN steps that are isostructural to Z-DNA CpG steps. Among their structural features, Z-steps are characterized by the presence of a lone pair…π contact that involves the stacking of the ribose O4′ atom of the first nucleotide with the 3′-face of the second nucleotide. Recently, it has been documented that the CpG step of the ubiquitous r(UNCG) tetraloops is a Z-step. Accordingly, such r(UNCG) conformations were called Z-turns. It has also been recognized that an r(GAAA) tetraloop in appropriate conditions can shapeshift to an unusual Z-turn conformation embedding an ApA Z-step. In this report, we explore the multiplicity of RNA motifs based on Z-steps by using the WebFR3D tool to which we added functionalities to be able to retrieve motifs containing lone pair…π contacts. Many examples that underscore the diversity and universality of these motifs are provided as well as tutorial guidance on using WebFR3D. In addition, this study provides an extensive survey of crystallographic, cryo-EM, NMR, and molecular dynamics studies on r(UNCG) tetraloops with a critical view on how to conduct database searches and exploit their results.

RNAsolo: a repository of cleaned PDB-derived RNA 3D structures

MOTIVATION

The development of algorithms dedicated to RNA 3D structures contributes to the demand for training, testing, and benchmarking data. A reliable source of such data derived from computational prediction is the RNA-Puzzles repository. In contrast, the largest resource with experimentally determined structures is the Protein Data Bank. However, files in this archive often contain other molecular data in addition to the RNA structure itself, which-to be used by RNA processing algorithms-should be removed.

RESULTS

RNAsolo is a self-updating database dedicated to RNA bioinformatics. It systematically collects experimentally determined RNA 3D structures stored in the PDB, cleans them from non-RNA chains, and groups them into equivalence classes. It allows users to download various subsets of data-clustered by resolution, source, data format, etc. - for further processing and analysis with a single click.

AVAILABILITY

The repository is publicly available at https://rnasolo.cs.put.poznan.pl.

Anhydrous Nucleic Acid Nanoparticles for Storage and Handling at Broad Range of Temperatures

Recent advances in nanotechnology now allow for the methodical implementation of therapeutic nucleic acids (TNAs) into modular nucleic acid nanoparticles (NANPs) with tunable physicochemical properties which can match the desired biological effects, provide uniformity, and regulate the delivery of multiple TNAs for combinatorial therapy. Despite the potential of novel NANPs, the maintenance of their structural integrity during storage and shipping remains a vital issue that impedes their broader applications. Cold chain storage is required to maintain the potency of NANPs in the liquid phase, which greatly increases transportation costs. To promote long-term storage and retention of biological activities at higher temperatures (e.g., +50 °C), a panel of representative NANPs is first exposed to three different drying mechanisms-vacuum concentration (SpeedVac), lyophilization (Lyo), and light-assisted drying (LAD)-and then rehydrated and analyzed. While SpeedVac primarily operates using heat, Lyo avoids temperature increases by taking advantage of pressure reduction and LAD involves a near-infrared laser for uniform drying in the presence of trehalose. This work compares and defines refinements crucial in formulating an optimal strategy for producing stable, fully functional NANPs and presents a forward advancement in their development for clinical applications.

CSSR: assignment of secondary structure to coarse-grained RNA tertiary structures

CSSR, an algorithm for assigning secondary structures to RNA 3D structures with missing atoms, has been developed. The base-pair assignment accuracy is close to 90% for 3D structures in which only one atom per nucleotide can be empirically identified.

RNA secondary-structure (rSS) assignment is one of the most routine forms of analysis of RNA 3D structures. However, traditional rSS assignment programs require full-atomic structures of the individual RNA nucleotides. This prevents their application to the modeling of RNA structures in which base atoms are missing. To address this issue, Coarse-grained Secondary Structure of RNA (CSSR), an algorithm for the assignment of rSS for structures in which nucleobase atomic positions are incomplete, has been developed. Using CSSR, an rSS assignment accuracy of ∼90% is achieved even for RNA structures in which only one backbone atom per nucleotide is known. Thus, CSSR will be useful for the analysis of experimentally determined and computationally predicted RNA 3D structures alike. The source code of CSSR is available at https://github.com/pylelab/CSSR.

On the predictibility of A-minor motifs from their local contexts

This study investigates the importance of the structural context in the formation of a type I/II A-minor motif. This very frequent structural motif has been shown to be important in the spatial folding of RNA molecules. We developed an automated method to classify A-minor motif occurrences according to their 3D context similarities, and we used a graph approach to represent both the structural A-minor motif occurrences and their classes at different scales. This approach leads us to uncover new subclasses of A-minor motif occurrences according to their local 3D similarities. The majority of classes are composed of homologous occurrences, but some of them are composed of non-homologous occurrences. The different classifications we obtain allow us to better understand the importance of the context in the formation of A-minor motifs. In a second step, we investigate how much knowledge of the context around an A-minor motif can help to infer its presence (and position). More specifically, we want to determine what kind of information, contained in the structural context, can be useful to characterize and predict A-minor motifs. We show that, for some A-minor motifs, the topology combined with a sequence signal is sufficient to predict the presence and the position of an A-minor motif occurrence. In most other cases, these signals are not sufficient for predicting the A-minor motif, however we show that they are good signals for this purpose. All the classification and prediction pipelines rely on automated processes, for which we describe the underlying algorithms and parameters.

Studies on Viroid Shed Light on the Role of RNA Three-Dimensional Structural Motifs in RNA Trafficking in Plants

RNAs play essential roles in various biological processes. Mounting evidence has demonstrated that RNA subcellular localization and intercellular/systemic trafficking govern their functions in coordinating plant growth at the organismal level. While numerous types of RNAs (i.e., mRNAs, small RNAs, rRNAs, tRNAs, and long noncoding RNAs) have been found to traffic in a non-cell-autonomous fashion within plants, the underlying regulatory mechanism remains unclear. Viroids are single-stranded circular noncoding RNAs, which entirely rely on their RNA motifs to exploit cellular machinery for organelle entry and exit, cell-to-cell movement through plasmodesmata, and systemic trafficking. Viroids represent an excellent model to dissect the role of RNA three-dimensional (3D) structural motifs in regulating RNA movement. Nearly two decades of studies have found multiple RNA 3D motifs responsible for viroid nuclear import as well as trafficking across diverse cellular boundaries in plants. These RNA 3D motifs function as "keys" to unlock cellular and subcellular barriers and guide RNA movement within a cell or between cells. Here, we summarize the key findings along this line of research with implications for future studies on RNA trafficking in plants.

RR3DD: an RNA global structure-based RNA three-dimensional structural classification database

The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.

Context-sensitivity of isosteric substitutions of non-Watson-Crick basepairs in recurrent RNA 3D motifs

Sequence variation in a widespread, recurrent, structured RNA 3D motif, the Sarcin/Ricin (S/R), was studied to address three related questions: First, how do the stabilities of structured RNA 3D motifs, composed of non-Watson–Crick (non-WC) basepairs, compare to WC-paired helices of similar length and sequence? Second, what are the effects on the stabilities of such motifs of isosteric and non-isosteric base substitutions in the non-WC pairs? And third, is there selection for particular base combinations in non-WC basepairs, depending on the temperature regime to which an organism adapts? A survey of large and small subunit rRNAs from organisms adapted to different temperatures revealed the presence of systematic sequence variations at many non-WC paired sites of S/R motifs. UV melting analysis and enzymatic digestion assays of oligonucleotides containing the motif suggest that more stable motifs tend to be more rigid. We further found that the base substitutions at non-Watson–Crick pairing sites can significantly affect the thermodynamic stabilities of S/R motifs and these effects are highly context specific indicating the importance of base-stacking and base-phosphate interactions on motif stability. This study highlights the significance of non-canonical base pairs and their contributions to modulating the stability and flexibility of RNA molecules.

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.

Graph Theoretical Methods and Workflows for Searching and Annotation of RNA Tertiary Base Motifs and Substructures

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.

RNAMotifContrast: a method to discover and visualize RNA structural motif subfamilies

Understanding the 3D structural properties of RNAs will play a critical role in identifying their functional characteristics and designing new RNAs for RNA-based therapeutics and nanotechnology. While several existing computational methods can help in the analysis of RNA properties by recognizing structural motifs, they do not provide the means to compare and contrast those motifs extensively. We have developed a new method, RNAMotifContrast, which focuses on analyzing the similarities and variations of RNA structural motif characteristics. In this method, a graph is formed to represent the similarities among motifs, and a new traversal algorithm is applied to generate visualizations of their structural properties. Analyzing the structural features among motifs, we have recognized and generalized the concept of motif subfamilies. To asses its effectiveness, we have applied RNAMotifContrast on a dataset of known RNA structural motif families. From the results, we observed that the derived subfamilies possess unique structural variations while holding standard features of the families. Overall, the visualization approach of this method presents a new perspective to observe the relation among motifs more closely, and the discovered subfamilies provide opportunities to achieve valuable insights into RNA’s diverse roles.

RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures

MOTIVATION

Applied research in machine learning progresses faster when a clean dataset is available and ready to use. Several datasets have been proposed and released over the years for specific tasks such as image classification, speech-recognition and more recently for protein structure prediction. However, for the fundamental problem of RNA structure prediction, information is spread between several databases depending on the level we are interested in: sequence, secondary structure, 3D structure or interactions with other macromolecules. In order to speed-up advances in machine-learning based approaches for RNA secondary and/or 3D structure prediction, a dataset integrating all this information is required, to avoid spending time on data gathering and cleaning.

RESULTS

Here, we propose the first attempt of a standardized and automatically generated dataset dedicated to RNA combining together: RNA sequences, homology information (under the form of position-specific scoring matrices) and information derived by annotation of available 3D structures (including secondary structure, canonical and non-canonical interactions and backbone torsion angles). The data are retrieved from public databases PDB, Rfam and SILVA. The paper describes the procedure to build such dataset and the RNA structure descriptors we provide. Some statistical descriptions of the resulting dataset are also provided.

AVAILABILITY AND IMPLEMENTATION

The dataset is updated every month and available online (in flat-text file format) on the EvryRNA software platform (https://evryrna.ibisc.univ-evry.fr/evryrna/rnanet). An efficient parallel pipeline to build the dataset is also provided for easy reproduction or modification.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

R2DT is a framework for predicting and visualising RNA secondary structure using templates

Non-coding RNAs (ncRNA) are essential for all life, and their functions often depend on their secondary (2D) and tertiary structure. Despite the abundance of software for the visualisation of ncRNAs, few automatically generate consistent and recognisable 2D layouts, which makes it challenging for users to construct, compare and analyse structures. Here, we present R2DT, a method for predicting and visualising a wide range of RNA structures in standardised layouts. R2DT is based on a library of 3,647 templates representing the majority of known structured RNAs. R2DT has been applied to ncRNA sequences from the RNAcentral database and produced >13 million diagrams, creating the world's largest RNA 2D structure dataset. The software is amenable to community expansion, and is freely available at https://github.com/rnacentral/R2DT and a web server is found at https://rnacentral.org/r2dt .

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.

Ribonucleic Acids (RNAs) are performing a broad range of essential molecular functions in cells, many of which rely on intricate folding properties of the molecule. Watson-Crick and Wobble base pairs form early, stack onto each other to create stems connected by loops, which are themselves stabilized by more sophisticated base interaction patterns. These networks are essential to shape RNA 3D structures but unfortunately still poorly understood. Here, we undertake the task to build a catalog of base interaction networks occurring in multiple structures. However, a pairwise comparison of all RNA structures is computationally heavy. Therefore, we devise an algorithm leveraging intrinsic properties of RNA base interaction networks that enables us to quickly mine full databases of 3D structures. Compared to previous methods, our techniques bring the total running time of the analysis from months to hours while performing more general searches. The data collected though this work will benefit molecular evolution studies and serve in structure prediction tools.

Thermostability, Tunability, and Tenacity of RNA as Rubbery Anionic Polymeric Materials in Nanotechnology and Nanomedicine-Specific Cancer Targeting with Undetectable Toxicity

RNA nanotechnology is the bottom-up self-assembly of nanometer-scale architectures, resembling LEGOs, composed mainly of RNA. The ideal building material should be (1) versatile and controllable in shape and stoichiometry, (2) spontaneously self-assemble, and (3) thermodynamically, chemically, and enzymatically stable with a long shelf life. RNA building blocks exhibit each of the above. RNA is a polynucleic acid, making it a polymer, and its negative-charge prevents nonspecific binding to negatively charged cell membranes. The thermostability makes it suitable for logic gates, resistive memory, sensor set-ups, and NEM devices. RNA can be designed and manipulated with a level of simplicity of DNA while displaying versatile structure and enzyme activity of proteins. RNA can fold into single-stranded loops or bulges to serve as mounting dovetails for intermolecular or domain interactions without external linking dowels. RNA nanoparticles display rubber- and amoeba-like properties and are stretchable and shrinkable through multiple repeats, leading to enhanced tumor targeting and fast renal excretion to reduce toxicities. It was predicted in 2014 that RNA would be the third milestone in pharmaceutical drug development. The recent approval of several RNA drugs and COVID-19 mRNA vaccines by FDA suggests that this milestone is being realized. Here, we review the unique properties of RNA nanotechnology, summarize its recent advancements, describe its distinct attributes inside or outside the body and discuss potential applications in nanotechnology, medicine, and material science.

On the Nature of Nucleobase Stacking in RNA: A Comprehensive Survey of Its Structural Variability and a Systematic Classification of Associated Interactions

The astonishing diversity in folding patterns of RNA three-dimensional (3D) structures is crafted by myriads of noncovalent contacts, of which base pairing and stacking are the most prominent. A systematic and comprehensive classification and annotation of these interactions is necessary for a molecular-level understanding of their roles. However, unlike in the case of base pairing, where a widely accepted nomenclature and classification scheme exists in the public domain, currently available classification schemes for base-base stacking need major enhancements to comprehensively capture the necessary features underlying the rich stacking diversity in RNA. Here, we extend the previous stacking classification based on nucleobase interacting faces by introducing a structurally intuitive geometry-cum topology-based scheme. Specifically, a stack is first classified in terms of the geometry described by the relative orientation of the glycosidic bonds, which generates eight basic stacking geometric families for heterodimeric stacks and six of those for homodimeric stacks. Further annotation in terms of the identity of the bases and the region of involvement of purines (five-membered, six-membered, or both rings) leads to the enumeration of 384 distinct RNA base stacks. Based on our classification scheme, we present an algorithm for automated identification of stacks in RNA crystal structures and analyze the stacking context in selected RNA structures. Overall, the work described here is expected to greatly facilitate the structure-based RNA research.

Neocles B. Leontis (1955 - 2020)

None.

Deciphering LncRNA-protein interactions using docking complexes

Deciphering RNA-protein interactions are important to study principal biological mechanisms including transcription and translation regulation, gene silencing, among others. Predicting RNA molecule interaction with the target protein could allow us to understand important cellular processes and design novel treatment therapies for various diseases. As non-coding RNAs do not have coding potential our knowledge about their functions is still limited. Therefore, RNA-binding proteins of non-coding RNAs regulating functions, viz. including cellular maturation, nuclear export and stability may play a very important role. Keeping in view of the need for refined methods to understand protein-RNA interactions, we have attempted a docking model to infer binding sites between lncRNA NONHSAT02007 and protein KIF13A for a rare disease phenotype that we are studying in our lab.Communicated by Ramaswamy H. Sarma.

RNA CoSSMos 2.0: an improved searchable database of secondary structure motifs in RNA three-dimensional structures

The RNA Characterization of Secondary Structure Motifs, RNA CoSSMos, database is a freely accessible online database that allows users to identify secondary structure motifs among RNA 3D structures and explore their structural features. RNA CoSSMos 2.0 now requires two closing base pairs for all RNA loop motifs to create a less redundant database of secondary structures. Furthermore, RNA CoSSMos 2.0 represents an upgraded database with new features that summarize search findings and aid in the search for 3D structural patterns among RNA secondary structure motifs. Previously, users were limited to viewing search results individually, with no built-in tools to compare search results. RNA CoSSMos 2.0 provides two new features, allowing users to summarize, analyze and compare their search result findings. A function has been added to the website that calculates the average and representative structures of the search results. Additionally, users can now view a summary page of their search results that reports percentages of each structural feature found, including sugar pucker, glycosidic linkage, hydrogen bonding patterns and stacking interactions. Other upgrades include a newly embedded NGL structural viewer, the option to download the clipped structure coordinates in *.pdb format and improved NMR structure results. RNA CoSSMos 2.0 is no longer simply a search engine for a structure database; it now has the capability of analyzing, comparing and summarizing search results.

Database URL: http://rnacossmos.com

Nucleic acid therapeutics: a focus on the development of aptamers

INTRODUCTION

Aptamers provide exciting opportunities for the development of specific and targeted therapeutic approaches.

AREAS COVERED

In this review, the authors discuss different therapeutic options available with nucleic acids, including aptamers, focussing on similarities and differences between them. The authors concentrate on case studies with specific aptamers, which exemplify their distinct advantages. The reasons for failure, wherever available, are deliberated upon. Attempts to accelerate the in vitro selection process have been discussed. Challenges with aptamers in terms of their specificity and targeted delivery and strategies to overcome these are described. Examples of precise regulation of systemic half-life of aptamers using antidotes are discussed.

EXPERT OPINION

Despite their nontoxic nature, a variety of reasons limit the therapeutic potential of aptamers in the clinic. The analysis of adverse effects observed with the pegnivacogin/anivamersen pair has highlighted the need to screen for preexisting PEG antibodies in any clinical trial involving pegylated molecules. Surprisingly, and promisingly, the ability of nucleic acid therapeutics to breach the blood brain barrier seems achievable. The recognition of specific motifs, e.g. G-quadruplex in thrombin-binding aptamers, or a 'nucleation' zone while designing aptamer-antidote pairs, is likely to accelerate the discovery of therapeutically efficacious molecules.

Simulation Study of the Plasticity of k-Turn Motif in Different Environments

The k-turn is a widespread and important motif in RNA. According to the internal hydrogen bond network, it has two stable states, called N1 and N3. The relative stability between the states changes with the environment. It is able to accept different conformations in different environments. This is called the "plasticity" of a molecule. In this work, we study the plasticity of k-turn by the mixing REMD method in explicit solvent. The results are concluded as follows. First, N1 and N3 are almost equally stable when k-turn is in the solvent alone. The molecule is quite flexible as a hinge. However, after binding to different proteins, such as the proteins L7Ae and L24e, k-turn falls into one global minimum. The preferred state could be either N1 or N3. On the contrary, the other nonpreferred state becomes unstable with a weaker binding affinity to the protein. It reveals that RNA-binding protein is able to modulate the representative state of k-turn at equilibrium. This is in agreement with the findings in experiments. Moreover, free energy calculations show that the free energy barrier between the N1 and N3 states of k-turn increases in the complexes. The state-to-state transition is greatly impeded. We also give a deep discussion on the mechanism of the high plasticity of k-turn in different environments.

Augmented base pairing networks encode RNA-small molecule binding preferences

RNA-small molecule binding is a key regulatory mechanism which can stabilize 3D structures and activate molecular functions. The discovery of RNA-targeting compounds is thus a current topic of interest for novel therapies. Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step towards understanding the interactions which drive binding specificity. Our tool, RNAmigos, builds and encodes a network representation of RNA structures to predict likely ligands for novel binding sites. We subject ligand predictions to virtual screening and show that we are able to place the true ligand in the 71st-73rd percentile in two decoy libraries, showing a significant improvement over several baselines, and a state of the art method. Furthermore, we observe that augmenting structural networks with non-canonical base pairing data is the only representation able to uncover a significant signal, suggesting that such interactions are a necessary source of binding specificity. We also find that pre-training with an auxiliary graph representation learning task significantly boosts performance of ligand prediction. This finding can serve as a general principle for RNA structure-function prediction when data is scarce. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights for possible applications to other structure-function learning tasks. The source code, data and a Web server are freely available at http://rnamigos.cs.mcgill.ca.

LocalSTAR3D: a local stack-based RNA 3D structural alignment tool

A fast-growing number of non-coding RNA structures have been resolved and deposited in Protein Data Bank (PDB). In contrast to the wide range of global alignment and motif search tools, there is still a lack of local alignment tools. Among all the global alignment tools for RNA 3D structures, STAR3D has become a valuable tool for its unprecedented speed and accuracy. STAR3D compares the 3D structures of RNA molecules using consecutive base-pairs (stacks) as anchors and generates an optimal global alignment. In this article, we developed a local RNA 3D structural alignment tool, named LocalSTAR3D, which was extended from STAR3D and designed to report multiple local alignments between two RNAs. The benchmarking results show that LocalSTAR3D has better accuracy and coverage than other local alignment tools. Furthermore, the utility of this tool has been demonstrated by rediscovering kink-turn motif instances, conserved domains in group II intron RNAs, and the tRNA mimicry of IRES RNAs.

Functional analysis reveals G/U pairs critical for replication and trafficking of an infectious non-coding viroid RNA

While G/U pairs are present in many RNAs, the lack of molecular studies to characterize the roles of multiple G/U pairs within a single RNA limits our understanding of their biological significance. From known RNA 3D structures, we observed that the probability a G/U will form a Watson-Crick (WC) base pair depends on sequence context. We analyzed 17 G/U pairs in the 359-nucleotide genome of Potato spindle tuber viroid (PSTVd), a circular non-coding RNA that replicates and spreads systemically in host plants. Most putative G/U base pairs were experimentally supported by selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE). Deep sequencing PSTVd genomes from plants inoculated with a cloned master sequence revealed naturally occurring variants, and showed that G/U pairs are maintained to the same extent as canonical WC base pairs. Comprehensive mutational analysis demonstrated that nearly all G/U pairs are critical for replication and/or systemic spread. Two selected G/U pairs were found to be required for PSTVd entry into, but not for exit from, the host vascular system. This study identifies critical roles for G/U pairs in the survival of an infectious RNA, and increases understanding of structure-based regulation of replication and trafficking of pathogen and cellular RNAs.

BiORSEO: a bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules

Motivation

RNA loops have been modelled and clustered from solved 3D structures into ordered collections of recurrent non-canonical interactions called ‘RNA modules’, available in databases. This work explores what information from such modules can be used to improve secondary structure prediction. We propose a bi-objective method for predicting RNA secondary structures by minimizing both an energy-based and a knowledge-based potential. The tool, called BiORSEO, outputs secondary structures corresponding to the optimal solutions from the Pareto set.

Results

We compare several approaches to predict secondary structures using inserted RNA modules information: two module data sources, Rna3Dmotif and the RNA 3D Motif Atlas, and different ways to score the module insertions: module size, module complexity or module probability according to models like JAR3D and BayesPairing. We benchmark them against a large set of known secondary structures, including some state-of-the-art tools, and comment on the usefulness of the half physics-based, half data-based approach.

Availability and implementation

The software is available for download on the EvryRNA website, as well as the datasets.

Supplementary information

Supplementary data are available at Bioinformatics online.

Automated, customizable and efficient identification of 3D base pair modules with BayesPairing

RNA structures possess multiple levels of structural organization. A secondary structure, made of Watson–Crick helices connected by loops, forms a scaffold for the tertiary structure. The 3D structures adopted by these loops are therefore critical determinants shaping the global 3D architecture. Earlier studies showed that these local 3D structures can be described as conserved sets of ordered non-Watson–Crick base pairs called RNA structural modules. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in the module databases. We present BayesPairing, an automated, efficient and customizable tool for (i) building Bayesian networks representing RNA 3D modules and (ii) rapid identification of 3D modules in sequences. BayesPairing uses a flexible definition of RNA 3D modules that allows us to consider complex architectures such as multi-branched loops and features multiple algorithmic improvements. We benchmarked our methods using cross-validation techniques on 3409 RNA chains and show that BayesPairing achieves up to ∼70% identification accuracy on module positions and base pair interactions. BayesPairing can handle a broader range of motifs (versatility) and offers considerable running time improvements (efficiency), opening the door to a broad range of large-scale applications.

Pairing interactions between nucleobases and ligands in aptamer:ligand complexes of riboswitches: crystal structure analysis, classification, optimal structures, and accurate interaction energies

In the present work, 67 crystal structures of the aptamer domains of RNA riboswitches are chosen for analysis of the structure and strength of hydrogen bonding (pairing) interactions between nucleobases constituting the aptamer binding pockets and the bound ligands. A total of 80 unique base:ligand hydrogen-bonded pairs containing at least two hydrogen bonds were identified through visual inspection. Classification of these contacts in terms of the interacting edge of the aptamer nucleobase revealed that interactions involving the Watson-Crick edge are the most common, followed by the sugar edge of purines and the Hoogsteen edge of uracil. Alternatively, classification in terms of the chemical constitution of the ligand yields five unique classes of base:ligand pairs: base:base, base:amino acid, base:sugar, base:phosphate, and base:other. Further, quantum mechanical (QM) geometry optimizations revealed that 67 out of 80 pairs exhibit stable geometries and optimal deviations from their macromolecular crystal occurrences. This indicates that these contacts are well-defined RNA aptamer:ligand interaction motifs. QM calculated interaction energies of base:ligand pairs reveal a rich hydrogen bonding landscape, ranging from weak interactions (base:other, -3 kcal/mol) to strong (base:phosphate, -48 kcal/mol) contacts. The analysis was further extended to study the biological importance of base:ligand interactions in the binding pocket of the tetrahydrofolate riboswitch and thiamine pyrophosphate riboswitch. Overall, our study helps in understanding the structural and energetic features of base:ligand pairs in riboswitches, which could aid in developing meaningful hypotheses in the context of RNA:ligand recognition. This can, in turn, contribute toward current efforts to develop antimicrobials that target RNAs.

RNApdbee 2.0: multifunctional tool for RNA structure annotation

In the field of RNA structural biology and bioinformatics, an access to correctly annotated RNA structure is of crucial importance, especially in the secondary and 3D structure predictions. RNApdbee webserver, introduced in 2014, primarily aimed to address the problem of RNA secondary structure extraction from the PDB files. Its new version, RNApdbee 2.0, is a highly advanced multifunctional tool for RNA structure annotation, revealing the relationship between RNA secondary and 3D structure given in the PDB or PDBx/mmCIF format. The upgraded version incorporates new algorithms for recognition and classification of high-ordered pseudoknots in large RNA structures. It allows analysis of isolated base pairs impact on RNA structure. It can visualize RNA secondary structures—including that of quadruplexes—with depiction of non-canonical interactions. It also annotates motifs to ease identification of stems, loops and single-stranded fragments in the input RNA structure. RNApdbee 2.0 is implemented as a publicly available webserver with an intuitive interface and can be freely accessed at http://rnapdbee.cs.put.poznan.pl/

In silico survey of the central conserved regions in viroids of the Pospiviroidae family for conserved asymmetric loop structures

Viroids are the smallest replicative pathogens, consisting of RNA circles (∼300 nucleotides) that require host machinery to replicate. Structural RNA elements recruit these host factors. Currently, many of these structural elements and the nature of their interactions are unknown. All Pospiviroidae have homology in the central conserved region (CCR). The CCR of potato spindle tuber viroid (PSTVd) contains a sarcin/ricin domain (SRD), the only viroid structural element with an unequivocal replication role. We assumed that every member of this family uses this region to recruit host factors, and that each CCR has an SRD-like asymmetric loop within it. Potential SRD or SRD-like motifs were sought in the CCR of each Pospiviroidae member as follows. Motif location in each CCR was predicted with MUSCLE alignment and Vienna RNAfold. Viroid-specific models of SRD-like motifs were built by superimposing noncanonical base pairs and nucleotides on a model of an SRD. The RNA geometry search engine FR3D was then used to find nucleotide groups close to the geometry suggested by this superimposition. Atomic resolution structures were assembled using the molecular visualization program Chimera, and the stability of each motif was assessed with molecular dynamics (MD). Some models required a protonated cytosine. To be stable within a cell, the pK_a of that cytosine must be shifted up. Constant pH-replica exchange MD analysis showed such a shift in the proposed structures. These data show that every Pospiviroidae member could form a motif that resembles an SRD in its CCR, and imply there could be undiscovered mimics of other RNA domains.

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

How to fold and protect mitochondrial ribosomal RNA with fewer guanines

Mammalian mitochondrial ribosomes evolved from bacterial ribosomes by reduction of ribosomal RNAs, increase of ribosomal protein content, and loss of guanine nucleotides. Guanine is the base most sensitive to oxidative damage. By systematically comparing high-quality, small ribosomal subunit RNA sequence alignments and solved 3D ribosome structures from mammalian mitochondria and bacteria, we deduce rules for folding a complex RNA with the remaining guanines shielded from solvent. Almost all conserved guanines in both bacterial and mammalian mitochondrial ribosomal RNA form guanine-specific, local or long-range, RNA–RNA or RNA–protein interactions. Many solvent-exposed guanines conserved in bacteria are replaced in mammalian mitochondria by bases less sensitive to oxidation. New guanines, conserved only in the mitochondrial alignment, are strategically positioned at solvent inaccessible sites to stabilize the ribosomal RNA structure. New mitochondrial proteins substitute for truncated RNA helices, maintain mutual spatial orientations of helices, compensate for lost RNA–RNA interactions, reduce solvent accessibility of bases, and replace guanines conserved in bacteria by forming specific amino acid–RNA interactions.