Study RNA Structure Research Articles

Synthesis of Nucleotides Bearing the 2'-O-Trifluoromethyl Group and Their Application in RNA Analogs Preparation.

The integration of fluorine atoms into biologically active organic compounds has proved to be a vital technique in small molecule drugs. This technique can substantially enhance crucial properties, including metabolic stability, lipophilicity, and bioavailability, often with a mere addition of a single fluorine atom or a trifluoromethyl group. Over the past few decades, this concept has also been applied in nucleic acid chemistry. A commonly employed 2'-OH substitution is the introduction of a 2'-deoxy-2'-fluoro (2'-F) group. The strong electronegativity of fluorine prompts the modified siRNA to readily adopt a C3'-endo conformation, resulting in significant advantages in terms of binding affinity. To enrich the toolbox of chemical modification of oligonucleotides, the replacement of the 2'-OH with the 2'-O-trifluoromethyl group has been developed in RNA analog synthesis. Oligodeoxynucleotides containing the 2'-O-trifluoromethyl group can greatly increase the thermal stability of DNA/RNA duplexes depending on the position and amount of the modification. Moreover, 2'-O-trifluoromethylated oligodeoxynucleotide also exhibited a slightly higher resistance to snake venom phosphodiesterase than the unmodified oligodeoxynucleotide. The 2'-O-trifluoromethylated oligonucleotides can emerge as a label to study RNA structure and function as well, or to develop DNA/RNA-based diagnostics. Hence, it is necessary to report an effective method for the synthesis, deprotection, purification, and characterization of oligonucleotides bearing a 2'-O-trifluoromethyl group. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Preparation of 6-N-benzoyl-5'-O-dimethoxytrityl-2'-O-trifluoromethyl adenosine 3'-(2-cyanoethyl N,N-diisopropyl)phosphoramidite Basic Protocol 2: Preparation of 4-N-acetyl-5'-O-dimethoxytrityl-2'-O-trifluoromethyl cytidine 3'-(2-cyanoethyl N,N-diisopropyl)phosphoramidite Basic Protocol 3: Preparation of 2-N-isobutyryl-5'-O-dimethoxytrityl-2'-O-trifluoromethyl guanine 3'-(2-cyanoethyl N,N-diisopropyl)phosphoramidite Basic Protocol 4: Preparation of 5'-O-dimethoxytrityl-2'-O-2-trifluoromethyl uridine 3'-(2-cyanoethyl N,N-diisopropyl) phosphoramidite Basic Protocol 5: Solid-phase synthesis of 2'-O-trifluoromethylated RNA analogs Basic Protocol 6: Deprotection and purification of 2'-O-trifluoromethyl-RNAs.

Dissecting the role of GGGGCC repeat RNA secondary structure in cellular toxicity associated with C9 FTD/ALS

AbstractBackgroundAberrant expansions of simple nucleotide repeat sequences can cause more than fifty human diseases. For example, the most common genetic cause of Frontotemporal dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS) is the expansion of an intronic GGGGCC hexanucleotide repeat sequence in C9orf72 gene (C9 FTD/ALS). Transcribed GGGGCC repeat RNAs can form complex secondary structures, which facilitate unusual interactions with cellular RNA‐binding proteins (RBPs). Sequestration of crucial RBPs into nuclear foci can inhibit the normal functions of those proteins. In addition, expanded GGGGCC repeat RNAs can support the translation of highly toxic repetitive peptides through a non‐canonical process known as repeat associated non‐AUG translation (RAN translation), which may require some specialized factors. Here we sought to understand how RNA secondary structure influence GGGGCC RAN translation and repeat elicited toxicity in C9 FTD/ALS.MethodWe used chemical modification coupled with traditional high‐throughput sequencing and nano‐pore sequencing approaches to probe GGGGCC repeat RNA secondary structures inside cells. We further use in vitro and in vivo RAN translation assays to determine whether repeat RNA secondary structures influence RAN translation efficiency.ResultOur RNA structural studies revealed that GGGGCC repeat RNAs form primarily form hairpin structures in cells. With nanopore sequencing, we were also able to detect the possible presence of unfolded and/or g‐quadruplex folded GGGGCC repeat RNAs in cells. Folding GGGGCC RNAs into hairpins supports RAN translation whereas folding repeat RNAs into g‐quadruplex structures strongly inhibits RAN translation in both in vitro and in vivo assays. Our results suggest that RNA secondary structure can affect the RAN translation elongation efficiency and g‐quadruplex structures may lead to the termination of elongating ribosomes.ConclusionWe present a deep sequencing‐based chemical probing technique to evaluate the roles of repeat RNA secondary structures in RNA toxicity underlying dementia‐associated repeat expansion disorders. Repeat RNA structural information can be used to identify chemical compounds that can bind to the RNA and prevent cytotoxicity in cells.

A Convenient Method for the Synthesis of 2′‐O‐Cyanoethylated Nucleotides and Their Application in the Solid‐Phase Synthesis of Related RNA Analogs

AbstractAlthough small interfering RNA (siRNA) is a key player among gene inhibition therapeutics, there are many obstacles to the development of siRNA drugs due to inherent properties of oligonucleotides, including the unsatisfactory stability of unmodified siRNA, poor pharmacokinetic distribution, and the toxicity induced by off‐target effects. To maximize treatment potency, chemical modification of siRNA has undoubtedly been the most successful strategy by far. Widely applied modifications include phosphorothioate linkages, 2′‐O‐methyl modifications, and 2′‐fluoro modifications, among others. To extend the family of chemical modifications for oligonucleotides, 2′‐O‐cyanoethylated RNA analogs were developed through the replacement of the 2′‐hydroxyl group with a 2′‐O‐cyanoethyl group (‐OCH2CH2CN). This modification can provide several advantages over unmodified RNA, such as increased stability, improved binding affinity to complementary DNA or RNA strands, and resistance to degradation by cellular nucleases. The 2′‐O‐cyanoethyl‐modified RNAs not only are applied in RNA silencing machinery but also act as research tools for studying RNA structure and function or for developing RNA‐based diagnostics. Therefore, the efficient synthesis, deprotection, purification, and characterization of 2′‐O‐cyanoethylated RNAs deserves more attention. This protocol describes the chemical synthesis of 2′‐O‐cyanoethylated nucleotides and the solid‐phase synthesis, deprotection, and purification of 2′‐O‐cyanoethylated RNAs. © 2023 Wiley Periodicals LLC.Basic Protocol 1: Preparation of 6‐N‐dimethylformamidyl‐5′‐O‐dimethoxytrityl‐2′‐O‐cyanoethyl adenosine 3′‐(2‐cyanoethyl N,N‐diisopropyl)phosphoramiditeBasic Protocol 2: Preparation of 4‐N‐acetyl‐5′‐O‐dimethoxytrityl‐2′‐O‐cyanoethyl cytidine 3′‐(2‐cyanoethyl N,N‐diisopropyl)phosphoramiditeBasic Protocol 3: Preparation of 2‐N‐dimethylformamidyl‐5′‐O‐dimethoxytrityl‐2′‐O‐cyanoethyl guanine 3′‐(2‐cyanoethyl N,N‐diisopropyl)phosphoramiditeBasic Protocol 4: Preparation of 5′‐O‐dimethoxytrityl‐2′‐O‐2‐cyanoethyl uridine 3′‐(2‐cyanoethyl N,N‐diisopropyl)phosphoramiditeBasic Protocol 5: Solid‐phase synthesis of 2′‐O‐cyanoethylated RNA analogsBasic Protocol 6: Deprotection and purification of synthesized 2′‐O‐cyanoethyl‐RNAs

Repeat RNA structure and protein interactions influence RAN translation and neurodegenerative phenotypes in models of FTD and ALS

AbstractBackgroundThe most common genetic cause of Frontotemporal dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS) is an intronic GGGGCC hexanucleotide repeat expansion in C9orf72 (C9FTD/ALS). Transcribed GGGGCC repeat RNAs sequester RNA‐binding proteins (RBPs) into nuclear and cytoplasmic foci, which inhibits their normal functions. GGGGCC repeat RNAs also support translation of toxic repeat peptides through a process known as repeat associated non‐AUG (RAN) translation. Here we sought to understand how repeat RNA secondary structure and RBP engagements that occur during RNA transcription, processing, and trafficking influence GGGGCC RAN translation and repeat elicited toxicity, with a goal of identifying novel therapeutic targets.MethodWe developed a cellular RNA‐capture methodology coupled to mass spectrometry to identify GGGGCC repeat RNA‐interacting proteins within specific cellular compartments. In parallel, we used chemical approaches to probe GGGGCC repeat RNA secondary structures inside cells and RAN translation assays to determine whether repeat RNA secondary structures and/or interacting RBPs influence RAN translation efficiency. We employed Drosophila and neuronal models of repeat expansions to evaluate potential RBPs as disease modifiers.ResultOur RNA structural studies revealed that GGGGCC repeat RNAs form primarily hairpin structures, rather than G‐quadruplex structures, in cells. Folding repat RNAs into such hairpins supports RAN translation whereas folding repeat RNAs into g‐quadruplex structures strongly inhibits their translation. We identified multiple RBPs enriching in the nuclear fraction of GGGGCC RNA‐interactome, including hnRNPH1 and SR (serine/arginine‐rich domain) proteins, which are known components of the nuclear foci found in C9FTD/ALS patient cells. Our comparative analysis found that lowering SR protein expression or modifying its phosphorylation modified toxicity in Drosophila models of multiple repeat expansion disorders. Currently we are investigating how repeat RNA structures influence RBP interactions and whether structure dependent RBP interactions selectively modulate RAN translation.ConclusionWe present a comprehensive approach to evaluate the roles of repeat RNA secondary structures and critical RBPs in RNA toxicity underlying dementia‐associated repeat expansion disorders.

Assessment of tools for RNA secondary structure prediction and extraction: a final-user perspective

The study of RNA structure is fundamental to clarify the RNA molecular functioning. The flexible RNA nature, the huge number of expressed RNAs, and the variety of functions make it challenging to obtain a quantity of structural information comparable to what is already available for proteins. The in silico prediction of RNA 3D structures is of particular relevance, to understand the fundamental features of the structure-function relationship, because the 3D structure drives the molecular interaction with DNA or protein complexes. The quality of the prediction of the RNA 3D structure is determined by the knowledge of a properly predicted or measured secondary structure. In this paper, we comparatively evaluate computational tools to model RNA secondary structure, focusing our investigation, among the dozens of methods in literature, on tools which are freely available and implemented in web-server versions, providing a more direct access to the final users, not necessarily bioinformatics experts. Our focus is on assessing performances for long sequences, with the final aim of selecting best methods for perspective lncRNAs investigation. Indeed, among RNAs, the non-coding and long non-coding RNAs (lncRNAs, with sequence length larger than 200 nts) assume special relevance, due to their function in regulatory mechanisms, which is still largely unexplored in the case of lncRNAs. As lncRNA experimental structures are at present missing, other families of large RNAs are here used as test cases, to establish the reliability of predictive bioinformatics tools and their perspective applicability to the case of lncRNAs. Communicated by Ramaswamy H. Sarma

Using DMS-MaPseq to uncover the roles of DEAD-box proteins in ribosome assembly.

DMS (dimethylsulfate) is a time-tested chemical probe for nucleic acid secondary structure that has recently re-emerged as a powerful tool to study RNA structure and structural changes, by coupling it to high throughput sequencing techniques. This variant, termed DMS-MaPseq, allows for mapping of all RNAs in a cell at the same time. However, if an RNA adopts different structures, for example during the assembly of an RNA-protein complex, or as part of its functional cycle, then DMS-MaPseq cannot differentiate between these structures, and an ensemble average will be produced. This is especially challenging for long-lived RNAs, such as ribosomes, whose steady-state abundance far exceeds that of any assembly intermediates, rendering those inaccessible to DMS-MaPseq on total RNAs. These challenges can be overcome by purification of assembly intermediates stalled at specific assembly steps (or steps in the functional cycle), via a combination of affinity tags and mutants stalled at defined steps, and subsequent DMS probing of these intermediates. Interpretation of the differences in DMS accessibility is facilitated by additional structural information, e.g. from cryo-EM experiments, available for many functional RNAs. While this approach is generally useful for studying RNA folding or conformational changes within RNA-protein complexes, it can be particularly valuable for studying the role(s) of DEAD-box proteins, as these tend to lead to larger conformational rearrangements, often resulting from the release of an RNA-binding protein from a bound RNA. Here we provide an adaptation of the DMS-MaPseq protocol to study RNA conformational transitions during ribosome assembly, which addresses the challenges arising from the presence of many assembly intermediates, all at concentrations far below that of mature ribosomes. While this protocol was developed for the yeast S. cerevisiae, we anticipate that it should be readily transferable to other model organisms for which affinity purification has been established.

Recent advances in RNA structurome.

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers—including RNA processing, transport, localization, and mRNA translation—across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

Web-based platform for analysis of RNA folding from high throughput chemical probing data.

RNA structures play critical roles in regulating gene expression across all domains of life and viruses. Chemical probing methods coupled with massively parallel sequencing have revolutionized the RNA structure field by enabling the assessment of many structures in their native, physiological context. Previously, we developed Dimethyl-Sulfate-based Mutational Profiling and Sequencing (DMS-MaPseq), which uses DMS to label the Watson-Crick face of open and accessible adenine and cytosine bases in the RNA. We used this approach to determine the genome-wide structures of HIV-1 and SARS-CoV-2 in infected cells, which permitted uncovering new biology and identifying therapeutic targets. Due to the simplicity and ease of the experimental procedure, DMS-MaPseq has been adopted by labs worldwide. However, bioinformatic analysis remains a substantial hurdle for labs that often lack the necessary infrastructure and computational expertise. Here we present a modern web-based interface that automates the analysis of chemical probing profiles from raw sequencing files (http://rnadreem.org). The availability of a simple web-based platform for DMS-MaPseq analysis will dramatically expand studies of RNA structure and aid in the design of structure-based therapeutics.

Deep Learning in RNA Structure Studies.

Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.

The Potential Role of RNA Structure in Crop Molecular Breeding.

The continually growing human population creates a concomitantly increasing demand for nutritious crops with high yields. Advances in high throughput sequencing technologies have revealed the genetic architecture of major crops. This includes extensive information enabling comprehensive genetic markers for breeding selection, new gene discoveries, and novel gene regulatory strategies for crop editing. RNA structure is an important type of genetic feature, essential for post-transcriptional regulation of gene expression. Here, we summarize recent advances in genome-wide RNA structure studies in crops and review the associated RNA structure-mediated regulation of gene expression. We also discuss the functional importance of those single nucleotide variations that induce large RNA structure disparities. Lastly, we discuss the potential role of RNA structure in crop molecular breeding.

Sub-3-Å cryo-EM structure of RNA enabled by engineered homomeric self-assembly.

High-resolution structural studies are essential for understanding the folding and function of diverse RNAs. Herein, we present a nanoarchitectural engineering strategy for efficient structural determination of RNA-only structures using single-particle cryogenic electron microscopy (cryo-EM). This strategy-ROCK (RNA oligomerization-enabled cryo-EM via installing kissing loops)-involves installing kissing-loop sequences onto the functionally nonessential stems of RNAs for homomeric self-assembly into closed rings with multiplied molecular weights and mitigated structural flexibility. ROCK enables cryo-EM reconstruction of the Tetrahymena group I intron at 2.98-Å resolution overall (2.85 Å for the core), allowing de novo model building of the complete RNA, including the previously unknown peripheral domains. ROCK is further applied to two smaller RNAs-the Azoarcus group I intron and the FMN riboswitch, revealing the conformational change of the former and the bound ligand in the latter. ROCK holds promise to greatly facilitate the use of cryo-EM in RNA structural studies.

Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers.

The recent development and application of methods based on the general principle of "crosslinking and proximity ligation" (crosslink-ligation) are revolutionizing RNA structure studies in living cells. However, extracting structure information from such data presents unique challenges. Here, we introduce a set of computational tools for the systematic analysis of data from a wide variety of crosslink-ligation methods, specifically focusing on read mapping, alignment classification, and clustering. We design a new strategy to map short reads with irregular gaps at high sensitivity and specificity. Analysis of previously published data reveals distinct properties and bias caused by the crosslinking reactions. We perform rigorous and exhaustive classification of alignments and discover eight types of arrangements that provide distinct information on RNA structures and interactions. To deconvolve the dense and intertwined gapped alignments, we develop a network/graph-based tool Crosslinked RNA Secondary Structure Analysis using Network Techniques (CRSSANT), which enables clustering of gapped alignments and discovery of new alternative and dynamic conformations. We discover that multiple crosslinking and ligation events can occur on the same RNA, generating multisegment alignments to report complex high-level RNA structures and multi-RNA interactions. We find that alignments with overlapped segments are produced from potential homodimers and develop a new method for their de novo identification. Analysis of overlapping alignments revealed potential new homodimers in cellular noncoding RNAs and RNA virus genomes in the Picornaviridae family. Together, this suite of computational tools enables rapid and efficient analysis of RNA structure and interaction data in living cells.

In vivo structure of the Ty1 retrotransposon RNA genome.

Long terminal repeat (LTR)-retrotransposons constitute a significant part of eukaryotic genomes and influence their function and evolution. Like other RNA viruses, LTR-retrotransposons efficiently utilize their RNA genome to interact with host cell machinery during replication. Here, we provide the first genome-wide RNA secondary structure model for a LTR-retrotransposon in living cells. Using SHAPE probing, we explore the secondary structure of the yeast Ty1 retrotransposon RNA genome in its native in vivo state and under defined in vitro conditions. Comparative analyses reveal the strong impact of the cellular environment on folding of Ty1 RNA. In vivo, Ty1 genome RNA is significantly less structured and more dynamic but retains specific well-structured regions harboring functional cis-acting sequences. Ribosomes participate in the unfolding and remodeling of Ty1 RNA, and inhibition of translation initiation stabilizes Ty1 RNA structure. Together, our findings support the dual role of Ty1 genomic RNA as a template for protein synthesis and reverse transcription. This study also contributes to understanding how a complex multifunctional RNA genome folds in vivo, and strengthens the need for studying RNA structure in its natural cellular context.

DMS-MaPseq for Genome-Wide or Targeted RNA Structure Probing In Vitro and In Vivo.

DMS-MaPseq is a chemical probing method combined with high throughput sequencing used to study RNA structure. Here we present a flexible protocol for adherent and suspension mammalian cells to analyze RNA structure in vitro or in vivo. The protocol provides instruction on either a targeted sequencing of a lncRNA of interest or a transcriptome-wide approach that provides structural data on all expressed RNAs, including lncRNAs. This technique is particularly useful for comparing in vitro and in vivo structure of RNAs, determining how mutations and polymorphisms with phenotypic effects influence RNA structure and analyzing RNA structure across the entire transcriptome.

Prediction of RNA secondary structure based on stem region replacement using the RSRNA algorithm

RNA functions, including the regulation of various cellular activities, seem to be closely related to its structure. However, accurately predicting RNA secondary structures can be difficult. Structural prediction can be achieved by selecting stem areas that are suitable and compatible from stem pools. Here, we propose a method for predicting the secondary structure of non-coding RNA based on stem region substitution, which we named RSRNA. This method is compatible with nested RNA secondary structures, while reducing any randomness. Our algorithm had higher performance and prediction accuracy than other algorithms, which deems it more effective for future RNA structure studies.

Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures.

The discovery and design of biologically important RNA molecules is outpacing three-dimensional structural characterization. Here, we demonstrate that cryo-EM can routinely resolve maps of RNA-only systems and that these maps enable sub-nanometer resolution coordinate estimation when complemented with multidimensional chemical mapping and Rosetta DRRAFTER computational modeling. This hybrid ‘Ribosolve’ pipeline detects and falsifies homologies and conformational rearrangements in eleven previously unknown 119- to 338-nucleotide protein-free RNA structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length V. cholerae and F. nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and computer-designed ATP-TTR-3 aptamer with and without AMP. Simulation benchmarks, blind challenges, compensatory mutagenesis, cross-RNA homologies, and internal controls demonstrate that Ribosolve can accurately resolve the global architectures of RNA molecules, but does not resolve atomic details. These tests offer guidelines for making inferences in future RNA structural studies with similarly accelerated throughput.

Viral RNA structure analysis using DMS-MaPseq.

RNA structure is critically important to RNA viruses in every part of the replication cycle. RNA structure is also utilized by DNA viruses in order to regulate gene expression and interact with host factors. Advances in next-generation sequencing have greatly enhanced the utility of chemical probing in order to analyze RNA structure. This review will cover some recent viral RNA structural studies using chemical probing and next-generation sequencing as well as the advantages of dimethyl sulfate (DMS)-mutational profiling and sequencing (MaPseq). DMS-MaPseq is a robust assay that can easily modify RNA in vitro, in cell and in virion. A detailed protocol for whole-genome DMS-MaPseq from cells transfected with HIV-1 and the structure of TAR as determined by DMS-MaPseq is presented. DMS-MaPseq has the ability to answer a variety of integral questions about viral RNA, including how they change in different environments and when interacting with different host factors.

Identification of novel RNA design candidates by clustering the extended RNA-As-Graphs library

BackgroundWe re-evaluate our RNA-As-Graphs clustering approach, using our expanded graph library and new RNA structures, to identify potential RNA-like topologies for design. Our coarse-grained approach represents RNA secondary structures as tree and dual graphs, with vertices and edges corresponding to RNA helices and loops. The graph theoretical framework facilitates graph enumeration, partitioning, and clustering approaches to study RNA structure and its applications. MethodsClustering graph topologies based on features derived from graph Laplacian matrices and known RNA structures allows us to classify topologies into ‘existing’ or hypothetical, and the latter into, ‘RNA-like’ or ‘non RNA-like’ topologies. Here we update our list of existing tree graph topologies and RAG-3D database of atomic fragments to include newly determined RNA structures. We then use linear and quadratic regression, optionally with dimensionality reduction, to derive graph features and apply several clustering algorithms on our tree-graph library and recently expanded dual-graph library to classify them into the three groups. ResultsThe unsupervised PAM and K-means clustering approaches correctly classify 72–77% of all existing graph topologies and 75–82% of newly added ones as RNA-like. For supervised k-NN clustering, the cross-validation accuracy ranges from 57 to 81%. ConclusionsUsing linear regression with unsupervised clustering, or quadratic regression with supervised clustering, provides better accuracies than supervised/linear clustering. All accuracies are better than random, especially for newly added existing topologies, thus lending credibility to our approach. General significanceOur updated RAG-3D database and motif classification by clustering present new RNA substructures and RNA-like motifs as novel design candidates.

HiPR: High-throughput probabilistic RNA structure inference

Recent high-throughput structure-sensitive genome-wide sequencing-based assays have enabled large-scale studies of RNA structure, and robust transcriptome-wide computational prediction of individual RNA structures across RNA classes from these assays has potential to further improve the prediction accuracy. Here, we describe HiPR, a novel method for RNA structure prediction at single-nucleotide resolution that combines high-throughput structure probing data (DMS-seq, DMS-MaPseq) with a novel probabilistic folding algorithm. On validation data spanning a variety of RNA classes, HiPR often increases accuracy for predicting RNA structures, giving researchers new tools to study RNA structure.

Method identifies differences between refolded and native RNA

Studies of RNA structure often involve denaturing, purifying, and refolding the RNA. But the structures adopted after such manipulations may not accurately reflect the original structure the molecule had in a cell. Using a strategy called SHAPE-MaP, which uses acylation of the 2′-hydroxyl group to determine the conformational flexibility of individual nucleotides in an RNA sequence, Kevin Weeks and Elizabeth Dethoff of the University of North Carolina at Chapel Hill found that RNA from a dengue virus adopts different structures when it has been denatured and refolded than it does straight out of the virus. The refolded RNA was more highly structured and tended to adopt a single conformation, whereas the native RNA was more flexible and adopted multiple conformations (Biochemistry 2019, DOI: 10.1021/acs.biochem.8b01219). “Natural RNAs are likely to be more dynamic than those in many common biochemical experiments,” Weeks says. However, he also notes, the SHAPE-MaP experiment can be