RNAthor - fast, accurate normalization, visualization and statistical analysis of RNA probing data resolved by capillary electrophoresis

IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling

In addition to their sequence, multiple functions of RNAs are encoded within their structure, which is often difficult to solve using physico-chemical methods. Incorporating low-resolution experimental data such as chemical probing into computational prediction significantly enhances RNA structure modeling accuracy. While medium- and high-throughput RNA structure probing techniques are widely accessible, the subsequent analysis process can be cumbersome, involving multiple software and manual data manipulation. In addition, the relevant interpretation of the data requires proper parameterization of the software and a strict consistency in the analysis pipeline. To streamline such workflows, we introduce IPANEMAP Suite, a comprehensive platform that guides users from chemically probing raw data to visually informative secondary structure models. IPANEMAP Suite seamlessly integrates various experimental datasets and facilitates comparative analysis of RNA structures under different conditions (footprinting), aiding in the study of protein or small molecule interactions with RNA. Here, we show that the unique ability of IPANEMAP Suite to perform integrative modeling using several chemical probing datasets with phylogenetic data can be instrumental in obtaining accurate secondary structure models. The platform's project-based approach ensures full traceability and generates publication-quality outputs, simplifying the entire RNA structure analysis process. IPANEMAP Suite is freely available at https://github.com/Sargueil-CiTCoM/ipasuite under a GPL-3.0 license.

RNAtango: Analysing and comparing RNA 3D structures via torsional angles

RNA molecules, essential for viruses and living organisms, derive their pivotal functions from intricate 3D structures. To understand these structures, one can analyze torsion and pseudo-torsion angles, which describe rotations around bonds, whether real or virtual, thus capturing the RNA conformational flexibility. Such an analysis has been made possible by RNAtango, a web server introduced in this paper, that provides a trigonometric perspective on RNA 3D structures, giving insights into the variability of examined models and their alignment with reference targets. RNAtango offers comprehensive tools for calculating torsion and pseudo-torsion angles, generating angle statistics, comparing RNA structures based on backbone torsions, and assessing local and global structural similarities using trigonometric functions and angle measures. The system operates in three scenarios: single model analysis, model-versus-target comparison, and model-versus-model comparison, with results output in text and graphical formats. Compatible with all modern web browsers, RNAtango is accessible freely along with the source code. It supports researchers in accurately assessing structural similarities, which contributes to the precision and efficiency of RNA modeling.

Compact RNA sensors for increasingly complex functions of multiple inputs

Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs. Finally, we describe the OpenTB challenge, which elicited 85-nucleotide sensors that compute a score for diagnosing active tuberculosis, based on the ratio of products of three gene segments. Building on OpenTB design strategies, we created an algorithm Nucleologic that produces similarly compact sensors for the three-gene score based on RNA and DNA. These results open new avenues for diverse applications of compact, single molecule sensors previously limited by design complexity.

Machine learning for RNA 2D structure prediction benchmarked on experimental data

Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were repeatedly benchmarked on various datasets. The latter, on the other hand, have not yet undergone extensive analysis that could suggest to the user which algorithm best fits the problem to be solved. In this review, we compare 15 methods that predict the secondary structure of RNA, of which 6 are based on deep learning (DL), 3 on shallow learning (SL) and 6 control methods on non-ML approaches. We discuss the ML strategies implemented and perform three experiments in which we evaluate the prediction of (I) representatives of the RNA equivalence classes, (II) selected Rfam sequences and (III) RNAs from new Rfam families. We show that DL-based algorithms (such as SPOT-RNA and UFold) can outperform SL and traditional methods if the data distribution is similar in the training and testing set. However, when predicting 2D structures for new RNA families, the advantage of DL is no longer clear, and its performance is inferior or equal to that of SL and non-ML methods.

RNA Chemical Labeling with Site-Specific, Relative Quantification by Mass Spectrometry for the Structural Study of a Neomycin-Sensing Riboswitch Aptamer Domain

High-resolution mass spectrometry was used for the label-free, direct localization and relative quantification of CMC+ -modifications of a neomycin-sensing riboswitch aptamer domain in the absence and presence of the aminoglycoside ligands neomycin B, ribostamycin, and paromomycin. The chemical probing and MS data for the free riboswitch show high exposure to solvent of the uridine nucleobases U7, U8, U13, U14, U18 as part of the proposed internal and apical loops, but those of U10 and U21 as part of the proposed internal loop were found to be far less exposed than expected. Thus, our data are in better agreement with the proposed secondary structure of the riboswitch in complexes with aminoglycosides than with that of free RNA. For the riboswitch in complexes with neomycin B, ribostamycin, and paromomycin, we found highly similar CMC+ -modification patterns and excellent agreement with previous NMR studies. Differences between the chemical probing and MS data in the absence and presence of the aminoglycoside ligands were quantitative rather than qualitative (i. e., the same nucleobases were labeled, but to different extents) and can be rationalized by stabilization of both the proposed bulge and the apical loop by aminoglycoside binding. Our study shows that chemical probing and mass spectrometry can provide important structural information and complement other techniques such as NMR spectroscopy.

Cell Compartment-Specific Folding of Ty1 Long Terminal Repeat Retrotransposon RNA Genome

The structural transitions RNAs undergo during trafficking are not well understood. Here, we used the well-developed yeast Ty1 retrotransposon to provide the first structural model of genome (g) RNA in the nucleus from a retrovirus-like transposon. Through a detailed comparison of nuclear Ty1 gRNA structure with those established in the cytoplasm, virus-like particles (VLPs), and those synthesized in vitro, we detected Ty1 gRNA structural alterations that occur during retrotransposition. Full-length Ty1 gRNA serves as the mRNA for Gag and Gag-Pol proteins and as the genome that is reverse transcribed within VLPs. We show that about 60% of base pairs predicted for the nuclear Ty1 gRNA appear in the cytoplasm, and active translation does not account for such structural differences. Most of the shared base pairs are represented by short-range interactions, whereas the long-distance pairings seem unique for each compartment. Highly structured motifs tend to be preserved after nuclear export of Ty1 gRNA. In addition, our study highlights the important role of Ty1 Gag in mediating critical RNA-RNA interactions required for retrotransposition.

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions

In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.

In Vitro Methods to Decipher the Structure of Viral RNA Genomes

RNA viruses encode essential information in their genomes as conserved structural elements that are involved in efficient viral protein synthesis, replication, and encapsidation. These elements can also establish complex networks of RNA-RNA interactions, the so-called RNA interactome, to shape the viral genome and control different events during intracellular infection. In recent years, targeting these conserved structural elements has become a promising strategy for the development of new antiviral tools due to their sequence and structural conservation. In this context, RNA-based specific therapeutic strategies, such as the use of siRNAs have been extensively pursued to target the genome of different viruses. Importantly, siRNA-mediated targeting is not a straightforward approach and its efficiency is highly dependent on the structure of the target region. Therefore, the knowledge of the viral structure is critical for the identification of potentially good target sites. Here, we describe detailed protocols used in our laboratory for the in vitro study of the structure of viral RNA genomes. These protocols include DMS (dimethylsulfate) probing, SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) analysis, and HMX (2'-hydroxyl molecular interference). These methodologies involve the use of high-throughput analysis techniques that provide extensive information about the 3D folding of the RNA under study and the structural tuning derived from the interactome activity. They are therefore a good tool for the development of new RNA-based antiviral compounds.

Entanglements of structure elements revealed in RNA 3D models

Computational methods to predict RNA 3D structure have more and more practical applications in molecular biology and medicine. Therefore, it is crucial to intensify efforts to improve the accuracy and quality of predicted three-dimensional structures. A significant role in this is played by the RNA-Puzzles initiative that collects, evaluates, and shares RNAs built computationally within currently nearly 30 challenges. RNA-Puzzles datasets, subjected to multi-criteria analysis, allow revealing the strengths and weaknesses of computer prediction methods. Here, we study the issue of entangled RNA fragments in the predicted RNA 3D structure models. By entanglement, we mean an arrangement of two structural elements such that one of them passes through the other. We propose the classification of entanglements driven by their topology and components. It distinguishes two general classes, interlaces and lassos, and subclasses characterized by element types-loops, dinucleotide steps, open single-stranded fragments-and puncture multiplicity. Our computational pipeline for entanglement detection, applied for 1,017 non-redundant models from RNA-Puzzles, has shown the frequency of different entanglements and allowed identifying 138 structures with intersected assemblies.

Chemical and Enzymatic Probing of Viral RNAs: From Infancy to Maturity and Beyond

RNA molecules are key players in a variety of biological events, and this is particularly true for viral RNAs. To better understand the replication of those pathogens and try to block them, special attention has been paid to the structure of their RNAs. Methods to probe RNA structures have been developed since the 1960s; even if they have evolved over the years, they are still in use today and provide useful information on the folding of RNA molecules, including viral RNAs. The aim of this review is to offer a historical perspective on the structural probing methods used to decipher RNA structures before the development of the selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) methodology and to show how they have influenced the current probing techniques. Actually, these technological breakthroughs, which involved advanced detection methods, were made possible thanks to the development of next-generation sequencing (NGS) but also to the previous works accumulated in the field of structural RNA biology. Finally, we will also discuss how high-throughput SHAPE (hSHAPE) paved the way for the development of sophisticated RNA structural techniques.

RNA Binding Properties of the Ty1 LTR-Retrotransposon Gag Protein

A universal feature of retroelement propagation is the formation of distinct nucleoprotein complexes mediated by the Gag capsid protein. The Ty1 retrotransposon Gag protein from Saccharomyces cerevisiae lacks sequence homology with retroviral Gag, but is functionally related. In addition to capsid assembly functions, Ty1 Gag promotes Ty1 RNA dimerization and cyclization and initiation of reverse transcription. Direct interactions between Gag and retrotransposon genomic RNA (gRNA) are needed for Ty1 replication, and mutations in the RNA-binding domain disrupt nucleation of retrosomes and assembly of functional virus-like particles (VLPs). Unlike retroviral Gag, the specificity of Ty1 Gag-RNA interactions remain poorly understood. Here we use microscale thermophoresis (MST) and electrophoretic mobility shift assays (EMSA) to analyze interactions of immature and mature Ty1 Gag with RNAs. The salt-dependent experiments showed that Ty1 Gag binds with high and similar affinity to different RNAs. However, we observed a preferential interaction between Ty1 Gag and Ty1 RNA containing a packaging signal (Psi) in RNA competition analyses. We also uncover a relationship between Ty1 RNA structure and Gag binding involving the pseudoknot present on Ty1 gRNA. In all likelihood, the differences in Gag binding affinity detected in vitro only partially explain selective Ty1 RNA packaging into VLPs in vivo.