Understanding the errors of SHAPE-directed RNA structure modeling

Tertiary folds of the SL5 RNA from the 5' proximal region of SARS-CoV-2 and related coronaviruses

Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2), resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4 to 6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across these human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9 to 8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4 to 9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities and notable differences, with implications for potential protein-binding modes and therapeutic targets.

Tertiary folds of the SL5 RNA from the 5' proximal region of SARS-CoV-2 and related coronaviruses

Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically-determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus SARS-CoV-2, resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4-6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across the studied human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9-8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4-9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities, with implications for potential protein-binding modes and therapeutic targets.

Structural analysis of MALAT1 long noncoding RNA in cells and in evolution

Although not canonically polyadenylated, the long noncoding RNA MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) is stabilized by a highly conserved 76-nt triple helix structure on its 3' end. The entire MALAT1 transcript is over 8000 nt long in humans. The strongest structural conservation signal in MALAT1 (as measured by covariation of base pairs) is in the triple helix structure. Primary sequence analysis of covariation alone does not reveal the degree of structural conservation of the entire full-length transcript, however. Furthermore, RNA structure is often context dependent; RNA binding proteins that are differentially expressed in different cell types may alter structure. We investigate here the in-cell and cell-free structures of the full-length human and green monkey (Chlorocebus sabaeus) MALAT1 transcripts in multiple tissue-derived cell lines using SHAPE chemical probing. Our data reveal levels of uniform structural conservation in different cell lines, in cells and cell-free, and even between species, despite significant differences in primary sequence. The uniformity of the structural conservation across the entire transcript suggests that, despite seeing covariation signals only in the triple helix junction of the lncRNA, the rest of the transcript's structure is remarkably conserved, at least in primates and across multiple cell types and conditions.

Structural, Dynamical, and Entropic Differences between SARS-CoV and SARS-CoV-2 s2m Elements Using Molecular Dynamics Simulations

The functional role of the highly conserved stem-loop II motif (s2m) in SARS-CoV and SARS-CoV-2 in the viral lifecycle remains enigmatic and an intense area of research. Structure and dynamics of the s2m are key to establishing a structure-function connection, yet a full set of atomistic resolution coordinates is not available for SARS-CoV-2 s2m. Our work constructs three-dimensional coordinates consistent with NMR solution phase data for SARS-CoV-2 s2m and provides a comparative analysis with its counterpart SARS-CoV s2m. We employed initial coordinates based on PDB ID 1XJR for SARS-CoV s2m and two models for SARS-CoV-2 s2m: one based on 1XJR in which we introduced the mutations present in SARS-CoV-2 s2m and the second based on the available SARS-CoV-2 NMR NOE data supplemented with knowledge-based methods. For each of the three systems, 3.5 μs molecular dynamics simulations were used to sample the structure and dynamics, and principal component analysis (PCA) reduced the ensembles to hierarchal conformational substates for detailed analysis. Dilute solution simulations of SARS-CoV s2m demonstrate that the GNRA-like terminal pentaloop is rigidly defined by base stacking uniquely positioned for possible kissing dimer formation. However, the SARS-CoV-2 s2m simulation did not retain the reported crystallographic SARS-CoV motifs and the terminal loop expands to a highly dynamic "nonaloop." Increased flexibility and structural disorganization are observed for the larger terminal loop, where an entropic penalty is computed to explain the experimentally observed reduction in kissing complex formation. Overall, both SARS-CoV and SARS-CoV-2 s2m elements have a similarly pronounced L-shape due to different motif interactions. Our study establishes the atomistic three-dimensional structure and uncovers dynamic differences that arise from s2m sequence changes, which sets the stage for the interrogation of different mechanistic pathways of suspected biological function.

Lessons Learned and Yet-to-Be Learned on the Importance of RNA Structure in SARS-CoV-2 Replication

SARS-CoV-2, the etiological agent responsible for the COVID-19 pandemic, is a member of the virus family Coronaviridae, known for relatively extensive (~30-kb) RNA genomes that not only encode for numerous proteins but are also capable of forming elaborate structures. As highlighted in this review, these structures perform critical functions in various steps of the viral life cycle, ultimately impacting pathogenesis and transmissibility. We examine these elements in the context of coronavirus evolutionary history and future directions for curbing the spread of SARS-CoV-2 and other potential human coronaviruses. While we focus on structures supported by a variety of biochemical, biophysical, and/or computational methods, we also touch here on recent evidence for novel structures in both protein-coding and noncoding regions of the genome, including an assessment of the potential role for RNA structure in the controversial finding of SARS-CoV-2 integration in “long COVID” patients. This review aims to serve as a consolidation of previous works on coronavirus and more recent investigation of SARS-CoV-2, emphasizing the need for improved understanding of the role of RNA structure in the evolution and adaptation of these human viruses.

Characteristic chemical probing patterns of loop motifs improve prediction accuracy of RNA secondary structures

RNA structures play a fundamental role in nearly every aspect of cellular physiology and pathology. Gaining insights into the functions of RNA molecules requires accurate predictions of RNA secondary structures. However, the existing thermodynamic folding models remain less accurate than desired, even when chemical probing data, such as selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) reactivities, are used as restraints. Unlike most SHAPE-directed algorithms that only consider SHAPE restraints for base pairing, we extract two-dimensional structural features encoded in SHAPE data and establish robust relationships between characteristic SHAPE patterns and loop motifs of various types (hairpin, internal, and bulge) and lengths (2-11 nucleotides). Such characteristic SHAPE patterns are closely related to the sugar pucker conformations of loop residues. Based on these patterns, we propose a computational method, SHAPELoop, which refines the predicted results of the existing methods, thereby further improving their prediction accuracy. In addition, SHAPELoop can provide information about local or global structural rearrangements (including pseudoknots) and help researchers to easily test their hypothesized secondary structures.

Sieving RNA 3D Structures with SHAPE and Evaluating Mechanisms Driving Sequence-Dependent Reactivity Bias

Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing provides local RNA flexibility information at single-nucleotide resolution. In general, SHAPE is thought of as a secondary structure (2D) technology, but we find evidence that robust tertiary structure (3D) information is contained in SHAPE data. Here, we report a new model that achieves a higher correlation between SHAPE data and native RNA 3D structures than the previous 3D structure-SHAPE relationship model. Furthermore, we demonstrate that the new model improves our ability to discern between SHAPE-compatible and -incompatible structures on model decoys. After identifying sequence-dependent bias in SHAPE experiments, we propose a mechanism driving sequence-dependent bias in SHAPE experiments, using replica-exchange umbrella sampling simulations to confirm that the SHAPE sequence bias is largely explained by the stability of the unreacted SHAPE reagent in the binding pocket. Taken together, this work represents multiple practical advances in our mechanistic and predictive understanding of SHAPE technology.

Small molecule-RNA targeting: starting with the fundamentals

The structural and regulatory elements in therapeutically relevant RNAs offer many opportunities for targeting by small molecules, yet fundamental understanding of what drives selectivity in small molecule:RNA recognition has been a recurrent challenge. In particular, RNAs tend to be more dynamic and offer less chemical functionality than proteins, and biologically active ligands must compete with the highly abundant and highly structured RNA of the ribosome. Indeed, the only small molecule drug targeting RNA other than the ribosome was just approved in August 2020, and our recent survey of the literature revealed fewer than 150 reported chemical probes that target non-ribosomal RNA in biological systems. This Feature outlines our efforts to improve small molecule targeting strategies and gain fundamental insights into small molecule:RNA recognition by analyzing patterns in both RNA-biased small molecule chemical space and RNA topological space privileged for differentiation. First, we synthesized libraries based on RNA binding scaffolds that allowed us to reveal general principles in small molecule:recognition and to ask precise chemical questions about drivers of affinity and selectivity. Elaboration of these scaffolds has led to recognition of medicinally relevant RNA targets, including viral and long noncoding RNA structures. More globally, we identified physicochemical, structural, and spatial properties of biologically active RNA ligands that are distinct from those of protein-targeted ligands, and we have provided the dataset and associated analytical tools as part of a publicly available online platform to facilitate RNA ligand discovery. At the same time, we used pattern recognition protocols to identify RNA topologies that can be differentially recognized by small molecules and have elaborated this technique to visualize conformational changes in RNA secondary structure. These fundamental insights into the drivers of RNA recognition in vitro have led to functional targeting of RNA structures in biological systems. We hope that these initial guiding principles, as well as the approaches and assays developed in their pursuit, will enable rapid progress toward the development of RNA-targeted chemical probes and ultimately new therapeutic approaches to a wide range of deadly human diseases.

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

Challenges and perspectives for structural biology of lncRNAs-the example of the Xist lncRNA A-repeats

Following the discovery of numerous long non-coding RNA (lncRNA) transcripts in the human genome, their important roles in biology and human disease are emerging. Recent progress in experimental methods has enabled the identification of structural features of lncRNAs. However, determining high-resolution structures is challenging as lncRNAs are expected to be dynamic and adopt multiple conformations, which may be modulated by interaction with protein binding partners. The X-inactive specific transcript (Xist) is necessary for X inactivation during dosage compensation in female placental mammals and one of the best-studied lncRNAs. Recent progress has provided new insights into the domain organization, molecular features, and RNA binding proteins that interact with distinct regions of Xist. The A-repeats located at the 5′ end of the transcript are of particular interest as they are essential for mediating silencing of the inactive X chromosome. Here, we discuss recent progress with elucidating structural features of the Xist lncRNA, focusing on the A-repeats. We discuss the experimental and computational approaches employed that have led to distinct structural models, likely reflecting the intrinsic dynamics of this RNA. The presence of multiple dynamic conformations may also play an important role in the formation of the associated RNPs, thus influencing the molecular mechanism underlying the biological function of the Xist A-repeats. We propose that integrative approaches that combine biochemical experiments and high-resolution structural biology in vitro with chemical probing and functional studies in vivo are required to unravel the molecular mechanisms of lncRNAs.

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.

Anomalous Reverse Transcription through Chemical Modifications in Polyadenosine Stretches

Thermostable reverse transcriptases are workhorse enzymes underlying nearly all modern techniques for RNA structure mapping and for the transcriptome-wide discovery of RNA chemical modifications. Despite their wide use, these enzymes' behaviors at chemical modified nucleotides remain poorly understood. Wellington-Oguri et al. recently reported an apparent loss of chemical modification within putatively unstructured polyadenosine stretches modified by dimethyl sulfate or 2' hydroxyl acylation, as probed by reverse transcription. Here, reanalysis of these and other publicly available data, capillary electrophoresis experiments on chemically modified RNAs, and nuclear magnetic resonance spectroscopy on (A)₁₂ and variants show that this effect is unlikely to arise from an unusual structure of polyadenosine. Instead, tests of different reverse transcriptases on chemically modified RNAs and molecules synthesized with single 1-methyladenosines implicate a previously uncharacterized reverse transcriptase behavior: near-quantitative bypass through chemical modifications within polyadenosine stretches. All tested natural and engineered reverse transcriptases (MMLV; SuperScript II, III, and IV; TGIRT-III; and MarathonRT) exhibit this anomalous bypass behavior. Accurate DMS-guided structure modeling of the polyadenylated HIV-1 3' untranslated region requires taking into account this anomaly. Our results suggest that poly(rA-dT) hybrid duplexes can trigger an unexpectedly effective reverse transcriptase bypass and that chemical modifications in mRNA poly(A) tails may be generally undercounted.

RNAdt: An online tutorial and data portal for the RNA structurome era

RNA is not only a passive transporter of genetic information, but also a pivotal player in all domains of life. RNA can regulate gene expression because of its involvement in transcription, mRNA modification and processing, and translation. RNA also possesses other intricate functions such as catalysis, ligand sensing, interaction with biomolecules, response to environment stresses, and information storage. The primary structure of RNA is single stranded, but it always folds into complex secondary and tertiary structures owing to base pairing and effects from the cellular environment. The importance of structure has been increasingly recognized in understanding the myriad functions of RNA. After decades of development, there is a wide range of RNA structure probing techniques. The marriage between structure probing and high-throughput sequencing (HTS) especially enables the measurement of RNA structure on a transcriptomic scale, advancing the advent of the RNA structurome era. Dozens of HTS-associated RNA structure probing methods have been published, so it is urgent to provide a user-friendly and easy-to-use resource for users who are perplexed by selecting the most suitable method for their experiments. Motivated by this demand, we collected currently available HTS-associated RNA structure probing methods and then developed RNAdt (freely accessible at http://www.zhounan.org/rnadt). RNAdt can be used as a web-based tutorial to learn fundamental knowledge of HTS-associated RNA structure probing methods. RNAdt can also be used as a data portal to access HTS data sets from previous RNA structurome studies. At the end of this work, we also provided perspectives on future development of RNA structure probing methods. Our study is expected to facilitate RNA structure probing and ultimately elucidate the connection between RNA structure and biological functions.

High-throughput determination of RNA structures

RNA performs and regulates a diverse range of cellular processes, with new functional roles being uncovered at a rapid pace. Interest is growing in how these functions are linked to RNA structures that form in the complex cellular environment. A growing suite of technologies that use advances in RNA structural probes, high-throughput sequencing and new computational approaches to interrogate RNA structure at unprecedented throughput are beginning to provide insights into RNA structures at new spatial, temporal and cellular scales.

Using Rosetta for RNA homology modeling

The three-dimensional structures of RNA molecules provide rich and often critical information for understanding their functions, including how they recognize small molecule and protein partners. Computational modeling of RNA 3D structure is becoming increasingly accurate, particularly with the availability of growing numbers of template structures already solved experimentally and the development of sequence alignment and 3D modeling tools to take advantage of this database. For several recent "RNA puzzle" blind modeling challenges, we have successfully identified useful template structures and achieved accurate structure predictions through homology modeling tools developed in the Rosetta software suite. We describe our semi-automated methodology here and walk through two illustrative examples: an adenine riboswitch aptamer, modeled from a template guanine riboswitch structure, and a SAM I/IV riboswitch aptamer, modeled from a template SAM I riboswitch structure.

RNA Structural Differentiation: Opportunities with Pattern Recognition

Our awareness and appreciation of the many regulatory roles of RNA have dramatically increased in the past decade. This understanding, in addition to the impact of RNA in many disease states, has renewed interest in developing selective RNA-targeted small molecule probes. However, the fundamental guiding principles in RNA molecular recognition that could accelerate these efforts remain elusive. While high-resolution structural characterization can provide invaluable insight, examples of well-characterized RNA structures, not to mention small molecule:RNA complexes, remain limited. This Perspective provides an overview of the current techniques used to understand RNA molecular recognition when high-resolution structural information is unavailable. We will place particular emphasis on a new method, pattern recognition of RNA with small molecules (PRRSM), that provides rapid insight into critical components of RNA recognition and differentiation by small molecules as well as into RNA structural features.

Quantitative Understanding of SHAPE Mechanism from RNA Structure and Dynamics Analysis

The selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) method probes RNA local structural and dynamic information at single nucleotide resolution. To gain quantitative insights into the relationship between nucleotide flexibility, RNA 3D structure, and SHAPE reactivity, we develop a 3D Structure-SHAPE Relationship model (3DSSR) to rebuild SHAPE profiles from 3D structures. The model starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. The 3DSSR model shows the relationship between SHAPE reactivity and RNA structure and energetics. Comparisons between the 3DSSR-predicted SHAPE profile and the experimental SHAPE data show correlation, suggesting that the extracted analytical function may have captured the key factors that determine the SHAPE reactivity profile. Furthermore, the theory offers an effective method to sieve RNA 3D models and exclude models that are incompatible with experimental SHAPE data.

Synergistic SHAPE/Single-Molecule Deconvolution of RNA Conformation under Physiological Conditions

Structural RNA domains are widely involved in the regulation of biological functions, such as gene expression, gene modification, and gene repair. Activity of these dynamic regions depends sensitively on the global fold of the RNA, in particular, on the binding affinity of individual conformations to effector molecules in solution. Consequently, both the 1) structure and 2) conformational dynamics of noncoding RNAs prove to be essential in understanding the coupling that results in biological function. Toward this end, we recently reported observation of three conformational states in the metal-induced folding pathway of the tRNA-like structure domain of Brome Mosaic Virus, via single-molecule fluorescence resonance energy transfer studies. We report herein selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE)-directed structure predictions as a function of metal ion concentrations ([Mn+]) to confirm the three-state folding model, as well as test 2° structure models from the literature. Specifically, SHAPE reactivity data mapped onto literature models agrees well with the secondary structures observed at 0-10 mM [Mg2+], with only minor discrepancies in the E hairpin domain at low [Mg2+]. SHAPE probing and SHAPE-directed structure predictions further confirm the stepwise unfolding pathway previously observed in our single-molecule studies. Of special relevance, this means that reduction in the metal-ion concentration unfolds the 3' pseudoknot interaction before unfolding the long-range stem interaction. This work highlights the synergistic power of combining 1) single-molecule Förster resonance energy transfer and 2) SHAPE-directed structure-probing studies for detailed analysis of multiple RNA conformational states. In particular, single-molecule guided deconvolution of the SHAPE reactivities permits 2° structure predictions of isolated RNA conformations, thereby substantially improving on traditional limitations associated with current structure prediction algorithms.

Classification of RNA structure change by 'gazing' at experimental data

Motivation

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2' Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by 'gel gazing.' SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human 'gazing' by identifying features of the SHAPE profile that human experts agree 'looks' like a riboSNitch.

Results

We find strong quantitative agreement between experts when RNA scientists 'gaze' at SHAPE data and identify riboSNitches. We identify dynamic time warping and seven other features predictive of the human consensus. The classSNitch classifier reported here accurately reproduces human consensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8. When we analyze 2019 mutant traces for 17 different RNAs, we find that features of the WT SHAPE reactivity allow us to improve thermodynamic structure predictions of riboSNitches. This is significant, as accurate RNA structural analysis and prediction is likely to become an important aspect of precision medicine.

Availability and Implementation

The classSNitch R package is freely available at http://classsnitch.r-forge.r-project.org .

Contact

alain@email.unc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Modern Approaches for Identification of Modified Nucleotides in RNA

This review considers approaches for detection of modified monomers in the RNA structure of living organisms. Recently, some data on dynamic alterations in the pool of modifications of the key RNA species that depend on external factors affecting the cells and physiological conditions of the whole organism have been accumulated. The recent studies have presented experimental data on relationship between the mechanisms of formation of modified/minor nucleotides of RNA in mammalian cells and the development of various pathologies. The development of novel methods for detection of chemical modifications of RNA nucleotides in the cells of living organisms and accumulation of knowledge on the contribution of modified monomers to metabolism and functioning of individual RNA species establish the basis for creation of novel diagnostic and therapeutic approaches. This review includes a short description of routine methods for determination of modified nucleotides in RNA and considers in detail modern approaches that enable not only detection but also quantitative assessment of the modification level of various nucleotides in individual RNA species.

Evaluating the quality of SHAPE data simulated by k-mers for RNA structure prediction

Finding an effective measure to predict a more accurate RNA secondary structure is a challenging problem. In the last decade, an experimental method, known as selective [Formula: see text]-hydroxyl acylation analyzed by primer extension (SHAPE), was proposed to measure the tendency of forming a base pair for almost all nucleotides in an RNA sequence. These SHAPE reactivities are then utilized to improve the accuracy of RNA structure prediction. Due to a significant impact of SHAPE reactivity and in order to reduce the experimental costs, we propose a new model called HL-k-mer. This model simulates the SHAPE reactivity for each nucleotide in an RNA sequence. This is done by fetching the SHAPE reactivities for all sub-sequences of length k (k-mers) appearing in helix and loop regions. For evaluating the quality of simulated SHAPE data, ESD-Fold method is used based on the SHAPE data simulated by the HL-k-mer model ([Formula: see text]). Also, for further evaluation of simulated SHAPE data, three different methods are employed. We also extend this model to simulate the SHAPE data for the RNA pseudoknotted structure. The results indicate that the average accuracies of prediction using the SHAPE data simulated by our models (for [Formula: see text]) are higher compared to the experimental SHAPE data.

LncRNA secondary structure in the cardiovascular system

Long non-coding RNAs (lncRNAs) have been increasingly studied during the past decade. This led to an immense number of annotated transcripts, out of which many were linked to a diverse range of biological mechanisms and diseases. Due to the variety of their regulatory potential, they are seen as an important link in understanding complex epigenetic mechanisms. Prominent examples of lncRNAs in the cardiovascular system are ANRIL, Braveheart, MALAT1 and HOTAIR which have been excessively studied. But despite the impressive number of described transcripts, only a few examples are characterized functionally. One way to do this is to identify accessible structural domains in the RNA secondary structure which have the ability to bind to DNA, RNA or proteins. Through recent improvements in computational as well as experimental methods, this exploration of secondary structure became not only more efficient than traditional methods like crystallization, but also feasible to investigate whole genome RNA structures. The purpose of this review is to highlight the recent advances in secondary structure probing methods and how these can be applied in order to investigate the functional roles of lncRNAs in the cardiovascular system.

[Letter to the Editor] Incorrect assignment of affected nucleotides in footprinting/probing experiments

Address correspondence to Sergey Belikov or Lars Wieslander, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91 Stockholm, Sweden. E-mail: sergey.belikov@su.se or lars.wieslander@su.se.

RNA structure inference through chemical mapping after accidental or intentional mutations

Despite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical-mapping method called "mutate-and-map read out through next-generation sequencing" (M2-seq) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double-DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a neural-network-inspired algorithm called M2-net. Measurements on the P4-P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations

We introduce a coarse-grained RNA model for molecular dynamics simulations, RACER (RnA CoarsE-gRained). RACER achieves accurate native structure prediction for a number of RNAs (average RMSD of 2.93 Å) and the sequence-specific variation of free energy is in excellent agreement with experimentally measured stabilities (R2 = 0.93). Using RACER, we identified hydrogen-bonding (or base pairing), base stacking, and electrostatic interactions as essential driving forces for RNA folding. Also, we found that separating pairing vs. stacking interactions allowed RACER to distinguish folded vs. unfolded states. In RACER, base pairing and stacking interactions each provide an approximate stability of 3-4 kcal/mol for an A-form helix. RACER was developed based on PDB structural statistics and experimental thermodynamic data. In contrast with previous work, RACER implements a novel effective vdW potential energy function, which led us to re-parameterize hydrogen bond and electrostatic potential energy functions. Further, RACER is validated and optimized using a simulated annealing protocol to generate potential energy vs. RMSD landscapes. Finally, RACER is tested using extensive equilibrium pulling simulations (0.86 ms total) on eleven RNA sequences (hairpins and duplexes).

Identification of acylation products in SHAPE chemistry

SHAPE chemistry (selective 2'-hydroxyl acylation analyzed by primer extension) has been developed to specifically target flexible nucleotides (often unpaired nucleotides) independently to their purine or pyrimidine nature for RNA secondary structure determination. However, to the best of our knowledge, the structure of 2'-O-acylation products has never been confirmed by NMR or X-ray data. We have realized the acylation reactions between cNMP and NMIA under SHAPE chemistry conditions and identified the acylation products using standard NMR spectroscopy and LC-MS/MS experiments. For cAMP and cGMP, the major acylation product is the 2'-O-acylated compound (>99%). A trace amount of N-acylated cAMP has also been identified by LC-UV-MS². While for cCMP, the isolated acylation products are composed of 96% of 2'-O-acylated, 4% of N,O-diacylated, and trace amount of N-acylated compounds. In addition, the characterization of the major 2'-O-acylated compound by NMR showed slight differences in the conformation of the acylated sugar between the three cyclic nucleotides. This interesting result should be useful to explain some unexpected reactivity of the SHAPE chemistry.

RNA structure through multidimensional chemical mapping

The discoveries of myriad non-coding RNA molecules, each transiting through multiple flexible states in cells or virions, present major challenges for structure determination. Advances in high-throughput chemical mapping give new routes for characterizing entire transcriptomes in vivo, but the resulting one-dimensional data generally remain too information-poor to allow accurate de novo structure determination. Multidimensional chemical mapping (MCM) methods seek to address this challenge. Mutate-and-map (M2), RNA interaction groups by mutational profiling (RING-MaP and MaP-2D analysis) and multiplexed •OH cleavage analysis (MOHCA) measure how the chemical reactivities of every nucleotide in an RNA molecule change in response to modifications at every other nucleotide. A growing body of in vitro blind tests and compensatory mutation/rescue experiments indicate that MCM methods give consistently accurate secondary structures and global tertiary structures for ribozymes, ribosomal domains and ligand-bound riboswitch aptamers up to 200 nucleotides in length. Importantly, MCM analyses provide detailed information on structurally heterogeneous RNA states, such as ligand-free riboswitches that are functionally important but difficult to resolve with other approaches. The sequencing requirements of currently available MCM protocols scale at least quadratically with RNA length, precluding general application to transcriptomes or viral genomes at present. We propose a modify-cross-link-map (MXM) expansion to overcome this and other current limitations to resolving the in vivo 'RNA structurome'.

Small Molecule-Based Pattern Recognition To Classify RNA Structure

Three-dimensional RNA structures are notoriously difficult to determine, and the link between secondary structure and RNA conformation is only beginning to be understood. These challenges have hindered the identification of guiding principles for small molecule:RNA recognition. We herein demonstrate that the strong and differential binding ability of aminoglycosides to RNA structures can be used to classify five canonical RNA secondary structure motifs through principal component analysis (PCA). In these analyses, the aminoglycosides act as receptors, while RNA structures labeled with a benzofuranyluridine fluorophore act as analytes. Complete (100%) predictive ability for this RNA training set was achieved by incorporating two exhaustively guanidinylated aminoglycosides into the receptor library. The PCA was then externally validated using biologically relevant RNA constructs. In bulge-stem-loop constructs of HIV-1 transactivation response element (TAR) RNA, we achieved nucleotide-specific classification of two independent secondary structure motifs. Furthermore, examination of cheminformatic parameters and PCA loading factors revealed trends in aminoglycoside:RNA recognition, including the importance of shape-based discrimination, and suggested the potential for size and sequence discrimination within RNA structural motifs. These studies present a new approach to classifying RNA structure and provide direct evidence that RNA topology, in addition to sequence, is critical for the molecular recognition of RNA.

Evolutionary Algorithm for RNA Secondary Structure Prediction Based on Simulated SHAPE Data

Background

Non-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure. Recently, the obtained information from the SHAPE experiment greatly improves the accuracy of RNA secondary structure prediction by adding this information to the thermodynamic free energy as pseudo-free energy.

Method

In this paper, a new method is proposed to predict RNA secondary structure based on both free energy and SHAPE pseudo-free energy. For each RNA sequence, a population of secondary structures is constructed and their SHAPE data are simulated. Then, an evolutionary algorithm is used to improve each structure based on both free and pseudo-free energies. Finally, a structure with minimum summation of free and pseudo-free energies is considered as the predicted RNA secondary structure.

Results and Conclusions

Computationally simulating the SHAPE data for a given RNA sequence requires its secondary structure. Here, we overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and consequently improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments. The source code and web server of our proposed method are freely available at http://mostafa.ut.ac.ir/ESD-Fold/.

Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution

Selective 2′‐hydroxyl acylation analyzed by primer extension (SHAPE) provides information on RNA structure at single‐nucleotide resolution. It is most often used in conjunction with RNA secondary structure prediction algorithms as a probabilistic or thermodynamic restraint. With the recent advent of ultra‐high‐throughput approaches for collecting SHAPE data, the applications of this technology are extending beyond structure prediction. In this review, we discuss recent applications of SHAPE data in the transcriptomic context and how this new experimental paradigm is changing our understanding of these experiments and RNA folding in general. SHAPE experiments probe both the secondary and tertiary structure of an RNA, suggesting that model‐free approaches for within and comparative RNA structure analysis can provide significant structural insight without the need for a full structural model. New methods incorporating SHAPE at different nucleotide resolutions are required to parse these transcriptomic data sets to transcend secondary structure modeling with global structural metrics. These ‘multiscale’ approaches provide deeper insights into RNA global structure, evolution, and function in the cell. WIREs RNA 2017, 8:e1374. doi: 10.1002/wrna.1374

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Packaging of Mason-Pfizer monkey virus (MPMV) genomic RNA depends upon conserved long-range interactions (LRIs) between U5 and gag sequences

MPMV has great potential for development as a vector for gene therapy. In this respect, precisely defining the sequences and structural motifs that are important for dimerization and packaging of its genomic RNA (gRNA) are of utmost importance. A distinguishing feature of the MPMV gRNA packaging signal is two phylogenetically conserved long-range interactions (LRIs) between U5 and gag complementary sequences, LRI-I and LRI-II. To test their biological significance in the MPMV life cycle, we introduced mutations into these structural motifs and tested their effects on MPMV gRNA packaging and propagation. Furthermore, we probed the structure of key mutants using SHAPE (selective 2'hydroxyl acylation analyzed by primer extension). Disrupting base-pairing of the LRIs affected gRNA packaging and propagation, demonstrating their significance to the MPMV life cycle. A double mutant restoring a heterologous LRI-I was fully functional, whereas a similar LRI-II mutant failed to restore gRNA packaging and propagation. These results demonstrate that while LRI-I acts at the structural level, maintaining base-pairing is not sufficient for LRI-II function. In addition, in vitro RNA dimerization assays indicated that the loss of RNA packaging in LRI mutants could not be attributed to the defects in dimerization. Our findings suggest that U5-gag LRIs play an important architectural role in maintaining the structure of the 5' region of the MPMV gRNA, expanding the crucial role of LRIs to the nonlentiviral group of retroviruses.

Decoding the RNA structurome

Structures of RNA molecules are essential for their architectural, regulatory, and catalytic functions. Recent advances in high throughput sequencing enabled the development of methods for probing RNA structures on a transcriptome-wide scale-termed the RNA structurome. Here we review the state-of-the-art technologies for probing the RNA structurome, and highlight insights gained from these studies. We also point out the limits of current methods and discuss potential directions for future improvements.

Full-length RNA structure prediction of the HIV-1 genome reveals a conserved core domain

A distance constrained secondary structural model of the ≈10 kb RNA genome of the HIV-1 has been predicted but higher-order structures, involving long distance interactions, are currently unknown. We present the first global RNA secondary structure model for the HIV-1 genome, which integrates both comparative structure analysis and information from experimental data in a full-length prediction without distance constraints. Besides recovering known structural elements, we predict several novel structural elements that are conserved in HIV-1 evolution. Our results also indicate that the structure of the HIV-1 genome is highly variable in most regions, with a limited number of stable and conserved RNA secondary structures. Most interesting, a set of long distance interactions form a core organizing structure (COS) that organize the genome into three major structural domains. Despite overlapping protein-coding regions the COS is supported by a particular high frequency of compensatory base changes, suggesting functional importance for this element. This new structural element potentially organizes the whole genome into three major domains protruding from a conserved core structure with potential roles in replication and evolution for the virus.

Physics-based RNA structure prediction

Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains. Furthermore, RNA 3D structure prediction is hampered by the lack of the knowledge about the tertiary contacts and their thermodynamic parameters. Low-resolution structural modeling enables us to estimate the conformational entropies for a number of tertiary folds through rigorous statistical mechanical calculations. The models lead to 3D tertiary folds at coarse-grained level. The coarse-grained structures serve as the initial structures for all-atom molecular dynamics refinement to build the final all-atom 3D structures. In this paper, we present an overview of RNA computational models for secondary and tertiary structures’ predictions and then focus on a recently developed RNA statistical mechanical model—the Vfold model. The main emphasis is placed on the physics behind the models, including the treatment of the non-canonical interactions in secondary and tertiary structure modelings, and the correlations to RNA functions.

Consistent global structures of complex RNA states through multidimensional chemical mapping

Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OHCleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.

DOI:http://dx.doi.org/10.7554/eLife.07600.001

Our genetic material, in the form of molecules of DNA, provides instructions for many different processes in our cells. To issue these instructions, particular sections of DNA are copied to make a type of molecule called ribonucleic acid (RNA). Some of these RNA molecules contain instructions to make proteins, but others—known as non-coding RNAs—regulate the activity of genes in cells.

The genetic information within RNA is encoded by the sequence of four different chemical parts called ‘nucleotides’. RNA can exist as a single strand of nucleotides, but the nucleotides can also pair up in specific combinations to form sections of double-stranded RNA. Therefore, a single strand of non-coding RNA can fold into a complex three-dimensional shape that contains loops, twists, and bulges.

The three-dimensional structures of non-coding RNAs are crucial for their roles in cells, but the variety and complexity of shapes that they can form makes it technically difficult to study them. In 2008, researchers developed a new method called MOHCA that can map the positions of nucleotides that are close together in the three-dimensional structure. Highly reactive chemicals are attached to the nucleotides and these can react with, and damage, other nearby nucleotides. By detecting which nucleotides have been damaged, it is possible to map the positions of these nucleotides and decipher the structure of the RNA molecule using computer algorithms.

MOHCA is a promising approach, but the initial methods to find the damaged nucleotides were tedious and required specialized equipment. Now, Cheng, Das et al.—including some of the researchers involved in the 2008 work—have developed an improved version of MOHCA that uses readily available RNA sequencing techniques to find the damaged nucleotides. The RNA sequencing data are then analyzed by a new algorithm in the Rosetta computer modeling software.

Cheng, Das et al. used this newly developed ‘MOHCA-seq’ and Rosetta to reveal the structures of a human non-coding RNA and several other non-coding RNA molecules to a much higher level of detail than before. Together, MOHCA-seq and Rosetta provide a rapid method for researchers to decipher the three-dimensional structure of non-coding RNAs. This method is likely to speed up the analysis of the complex structures of non-coding RNAs. It will be useful in future efforts to work out what roles these RNAs play in cells, including their activity in cancer, neurodegeneration, and other diseases.

DOI:http://dx.doi.org/10.7554/eLife.07600.002

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

RNA secondary structure prediction based on SHAPE data in helix regions

RNA molecules play important and fundamental roles in biological processes. Frequently, the functional form of single-stranded RNA molecules requires a specific tertiary structure. Classically, RNA structure determination has mostly been accomplished by X-Ray crystallography or Nuclear Magnetic Resonance approaches. These experimental methods are time consuming and expensive. In the past two decades, some computational methods and algorithms have been developed for RNA secondary structure prediction. In these algorithms, minimum free energy is known as the best criterion. However, the results of algorithms show that minimum free energy is not a sufficient criterion to predict RNA secondary structure. These algorithms need some additional knowledge about the structure, which has to be added in the methods. Recently, the information obtained from some experimental data, called SHAPE, can greatly improve the consistency between the native and predicted RNA secondary structure. In this paper, we investigate the influence of SHAPE data on four types of RNA substructures, helices, loops, base pairs from the start and end of helices and two base pairs from the start and end of helices. The results show that SHAPE data in helix regions can improve the prediction. We represent a new method to apply SHAPE data in helix regions for finding RNA secondary structure. Finally, we compare the results of the method on a set of RNAs to predict minimum free energy structure based on considering all SHAPE data and only SHAPE data in helix regions as pseudo free energy and without SHAPE data (without any pseudo free energy). The results show that RNA secondary structure prediction based on considering only SHAPE data in helix regions is more successful than not considering SHAPE data and it provides competitive results in comparison with considering all SHAPE data.

Computational analysis of RNA structures with chemical probing data

RNAs play various roles, not only as the genetic codes to synthesize proteins, but also as the direct participants of biological functions determined by their underlying high-order structures. Although many computational methods have been proposed for analyzing RNA structures, their accuracy and efficiency are limited, especially when applied to the large RNAs and the genome-wide data sets. Recently, advances in parallel sequencing and high-throughput chemical probing technologies have prompted the development of numerous new algorithms, which can incorporate the auxiliary structural information obtained from those experiments. Their potential has been revealed by the secondary structure prediction of ribosomal RNAs and the genome-wide ncRNA function annotation. In this review, the existing probing-directed computational methods for RNA secondary and tertiary structure analysis are discussed.

RNA SHAPE chemistry with aromatic acylating reagents

As chemical methods for RNA secondary structure determination, SHAPE chemistry (selective 2'-hydroxyl acylation analyzed by primer extension) has been developed to specifically target flexible nucleotides (often unpaired nucleotides) independently to their purine or pyrimidine nature. In order to improve the specificity of acylating reagents towards unpaired nucleotides, we have explored the reactivity of symmetric anhydrides, acyl fluorides, active esters like succinimidyl ester and cyanomethyl esters for 2'-O-acylation reaction. Among the tested compounds, only the acyl fluoride 4 showed a low reactivity (compared to NMIA). However, this study is the first to show that nucleophilic catalysts like DMAP greatly improved the selective 2'-hydroxyl acylation by symmetric anhydrides, acyl fluorides and succinimidyl ester, with the 2-fluorobenzoic anhydride 5 being the most reactive.

Progress and outlook in structural biology of large viral RNAs

The field of viral molecular biology has reached a precipice for which pioneering studies on the structure of viral RNAs are beginning to bridge the gap. It has become clear that viral genomic RNAs are not simply carriers of hereditary information, but rather are active players in many critical stages during replication. Indeed, functions such as cap-independent translation initiation mechanisms are, in some cases, primarily driven by RNA structural determinants. Other stages including reverse transcription initiation in retroviruses, nuclear export and viral packaging are specifically dependent on the proper 3-dimensional folding of multiple RNA domains to recruit necessary viral and host factors required for activity. Furthermore, a large-scale conformational change within the 5'-untranslated region of HIV-1 has been proposed to regulate the temporal switch between viral protein synthesis and packaging. These RNA-dependent functions are necessary for replication of many human disease-causing viruses such as severe acute respiratory syndrome (SARS)-associated coronavirus, West Nile virus, and HIV-1. The potential for antiviral development is currently hindered by a poor understanding of RNA-driven molecular mechanisms, resulting from a lack of structural information on large RNAs and ribonucleoprotein complexes. Herein, we describe the recent progress that has been made on characterizing these large RNAs and provide brief descriptions of the techniques that will be at the forefront of future advances. Ongoing and future work will contribute to a more complete understanding of the lifecycles of retroviruses and RNA viruses and potentially lead to novel antiviral strategies.

SHAPE analysis of the RNA secondary structure of the Mouse Hepatitis Virus 5' untranslated region and N-terminal nsp1 coding sequences

SHAPE technology was used to analyze RNA secondary structure of the 5' most 474 nts of the MHV-A59 genome encompassing the minimal 5' cis-acting region required for defective interfering RNA replication. The structures generated were in agreement with previous characterizations of SL1 through SL4 and two recently predicted secondary structure elements, S5 and SL5A. SHAPE provided biochemical support for four additional stem-loops not previously functionally investigated in MHV. Secondary structure predictions for 5' regions of MHV-A59, BCoV and SARS-CoV were similar despite high sequence divergence. The pattern of SHAPE reactivity of in virio genomic RNA, ex virio genomic RNA, and in vitro synthesized RNA was similar, suggesting that binding of N protein or other proteins to virion RNA fails to protect the RNA from reaction with lipid permeable SHAPE reagent. Reverse genetic experiments suggested that SL5C and SL6 within the nsp1 coding sequence are not required for viral replication.

High-throughput mutate-map-rescue evaluates SHAPE-directed RNA structure and uncovers excited states

The three-dimensional conformations of noncoding RNAs underpin their biochemical functions but have largely eluded experimental characterization. Here, we report that integrating a classic mutation/rescue strategy with high-throughput chemical mapping enables rapid RNA structure inference with unusually strong validation. We revisit a 16S rRNA domain for which SHAPE (selective 2'-hydroxyl acylation with primer extension) and limited mutational analysis suggested a conformational change between apo- and holo-ribosome conformations. Computational support estimates, data from alternative chemical probes, and mutate-and-map (M(2)) experiments highlight issues of prior methodology and instead give a near-crystallographic secondary structure. Systematic interrogation of single base pairs via a high-throughput mutation/rescue approach then permits incisive validation and refinement of the M(2)-based secondary structure. The data further uncover the functional conformation as an excited state (20 ± 10% population) accessible via a single-nucleotide register shift. These results correct an erroneous SHAPE inference of a ribosomal conformational change, expose critical limitations of conventional structure mapping methods, and illustrate practical steps for more incisively dissecting RNA dynamic structure landscapes.

Probing viral genomic structure: alternative viewpoints and alternative structures for satellite tobacco mosaic virus RNA

Viral RNA structure prediction is a valuable tool for development of drugs against viral disease. This work discusses different approaches to predicting encapsidated viral RNA and highlights satellite tobacco mosaic virus (STMV) RNA as a model system with excellent crystallography data. Fundamentally important issues for debate include thermodynamic versus kinetic control of virus assembly and the possible consequences of quasi-species in the primary structure on RNA secondary structure prediction of a single structure or an ensemble of structures. Multiple computational tools and chemical reagents are now available for improved viral RNA structure prediction. Two different predicted structures for encapsidated STMV RNA result from differences in three main areas: a different approach and philosophy to studying encapsidated viral RNA, an emphasis on different RNA motifs, and technical differences in computational methods and chemical reagents. The experiments with traditional chemical probing and SHAPE reagents are compared in terms of chemistry, results, and interpretation for STMV RNA as well as other RNA protein assemblies, such as the 5'UTR of HIV and the ribosome. This discussion of the challenges of viral RNA structure prediction will lead to new experiments and improved future predictions for viral RNA.

SHAPE-Seq 2.0: systematic optimization and extension of high-throughput chemical probing of RNA secondary structure with next generation sequencing

RNA structure is a primary determinant of its function, and methods that merge chemical probing with next generation sequencing have created breakthroughs in the throughput and scale of RNA structure characterization. However, little work has been done to examine the effects of library preparation and sequencing on the measured chemical probe reactivities that encode RNA structural information. Here, we present the first analysis and optimization of these effects for selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). We first optimize SHAPE-Seq, and show that it provides highly reproducible reactivity data over a wide range of RNA structural contexts with no apparent biases. As part of this optimization, we present SHAPE-Seq v2.0, a ‘universal’ method that can obtain reactivity information for every nucleotide of an RNA without having to use or introduce a specific reverse transcriptase priming site within the RNA. We show that SHAPE-Seq v2.0 is highly reproducible, with reactivity data that can be used as constraints in RNA folding algorithms to predict structures on par with those generated using data from other SHAPE methods. We anticipate SHAPE-Seq v2.0 to be broadly applicable to understanding the RNA sequence–structure relationship at the heart of some of life's most fundamental processes.

RNA secondary structure modeling at consistent high accuracy using differential SHAPE

RNA secondary structure modeling is a challenging problem, and recent successes have raised the standards for accuracy, consistency, and tractability. Large increases in accuracy have been achieved by including data on reactivity toward chemical probes: Incorporation of 1M7 SHAPE reactivity data into an mfold-class algorithm results in median accuracies for base pair prediction that exceed 90%. However, a few RNA structures are modeled with significantly lower accuracy. Here, we show that incorporating differential reactivities from the NMIA and 1M6 reagents--which detect noncanonical and tertiary interactions--into prediction algorithms results in highly accurate secondary structure models for RNAs that were previously shown to be difficult to model. For these RNAs, 93% of accepted canonical base pairs were recovered in SHAPE-directed models. Discrepancies between accepted and modeled structures were small and appear to reflect genuine structural differences. Three-reagent SHAPE-directed modeling scales concisely to structurally complex RNAs to resolve the in-solution secondary structure analysis problem for many classes of RNA.

Standardization of RNA chemical mapping experiments

Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing. We show that simple dilutions, referencing standards (GAGUA hairpins), and HiTRACE/MAPseeker analysis allow rigorous overmodification correction, background subtraction, and normalization for electrophoretic data and a ligation bias correction needed for accurate deep sequencing data. Comparisons across six noncoding RNAs stringently test the proposed standardization of dimethyl sulfate (DMS), 2′-OH acylation (SHAPE), and carbodiimide measurements. Identification of new signatures for extrahelical bulges and DMS “hot spot” pockets (including tRNA A58, methylated in vivo) illustrates the utility and necessity of standardization for quantitative RNA mapping.

Highly conserved RNA pseudoknots at the Gag-Pol junction of HIV-1 suggest a novel mechanism of -1 ribosomal frameshifting

-1 programmed ribosomal frameshifting (PRF) is utilized by many viruses to synthesize their enzymatic (Pol) and structural (Gag) proteins at a defined ratio. For efficient -1 PRF, two cis-acting elements are required: a heptanucleotide frameshift site and a downstream stimulator such as a pseudoknot. We have analyzed the gag-pol junction sequences from 4254 HIV-1 strains. Approximately ninety-five percent of the sequences can form four pseudoknots PK1-PK4 (∼ 97% contain PK1, PK3, and PK4), covering ∼ 72 nt including the frameshift site. Some pseudoknots are mutually excluded due to sequence overlap. PK1 and PK3 arrange tandemly. Their stems form a quasi-continuous helix of ∼ 22 bp. We propose a novel mechanism for possible roles of these pseudoknots. Multiple alternative structures may exist at the gag-pol junction. In most strains, the PK1-PK3 tandem pseudoknots may dominate the structurally heterogeneous pool of RNA due to their greater overall stability. The tandem pseudoknots may function as a breaking system to slow down the ribosome. The ribosome unwinds PK1 and stem 1 of PK3 before it can reach the frameshift site. Then, PK4 can form rapidly because the intact stem 2 of PK3 makes up a large part of the stem 1 of PK4. The newly formed PK4 jams the entrance of the mRNA tunnel. The process then proceeds as in a typical case of -1 PRF. This mechanism incorporates several exquisite new features while still being consistent with the current paradigm of pseudoknot-dependent -1 PRF.

RNA design rules from a massive open laboratory

Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models--even at the secondary structure level--hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies--including several previously unrecognized negative design rules--were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

The mutate-and-map protocol for inferring base pairs in structured RNA

Chemical mapping is a widespread technique for structural analysis of nucleic acids in which a molecule's reactivity to different probes is quantified at single nucleotide resolution and used to constrain structural modeling. This experimental framework has been extensively revisited in the past decade with new strategies for high-throughput readouts, chemical modification, and rapid data analysis. Recently, we have coupled the technique to high-throughput mutagenesis. Point mutations of a base paired nucleotide can lead to exposure of not only that nucleotide but also its interaction partner. Systematically carrying out the mutation and mapping for the entire system gives an experimental approximation of the molecule's "contact map." Here, we give our in-house protocol for this "mutate-and-map" (M2) strategy, based on 96-well capillary electrophoresis, and we provide practical tips on interpreting the data to infer nucleic acid structure.