Visualizing RNA conformational and architectural heterogeneity in solution

RNA sample optimization for cryo-EM analysis

RNAs play critical roles in most biological processes. Although the three-dimensional (3D) structures of RNAs primarily determine their functions, it remains challenging to experimentally determine these 3D structures due to their conformational heterogeneity and intrinsic dynamics. Cryogenic electron microscopy (cryo-EM) has recently played an emerging role in resolving dynamic conformational changes and understanding structure-function relationships of RNAs including ribozymes, riboswitches and bacterial and viral noncoding RNAs. A variety of methods and pipelines have been developed to facilitate cryo-EM structure determination of challenging RNA targets with small molecular weights at subnanometer to near-atomic resolutions. While a wide range of conditions have been used to prepare RNAs for cryo-EM analysis, correlations between the variables in these conditions and cryo-EM visualizations and reconstructions remain underexplored, which continue to hinder optimizations of RNA samples for high-resolution cryo-EM structure determination. Here we present a protocol that describes rigorous screenings and iterative optimizations of RNA preparation conditions that facilitate cryo-EM structure determination, supplemented by cryo-EM data processing pipelines that resolve RNA dynamics and conformational changes and RNA modeling algorithms that generate atomic coordinates based on moderate- to high-resolution cryo-EM density maps. The current protocol is designed for users with basic skills and experience in RNA biochemistry, cryo-EM and RNA modeling. The expected time to carry out this protocol may range from 3 days to more than 3 weeks, depending on the many variables described in the protocol. For particularly challenging RNA targets, this protocol could also serve as a starting point for further optimizations.

Retention mechanism in slalom chromatography: Perspectives on the characterization of large DNA and RNA biopolymers in cell and gene therapy

Significant progress has been made in the last two decades in producing small (<2μm), high-purity, and low-adsorption particles, columns and system hardware, for ultra-high pressure liquid chromatography (UHPLC). Simultaneously, the recent rapid expansion of cell and gene therapies for treating diseases necessitates novel analytical technologies for analyzing large (>2 kbp) plasmid double-stranded (ds) DNA (which encodes for the in vitro transcription (IVT) of single-stranded (ss) mRNA therapeutics) and dsRNAs (related to IVT production impurities) biopolymers. In this context, slalom chromatography (SC), a retention mode co-discovered in 1988, is being revitalized using the most advanced column technologies for improved determination of the critical quality attributes (CQAs) of such new therapeutics. In this review, we first recall non-exhaustively the main currently available analytical techniques (enzyme-linked immunosorbent assay (ELISA), agarose gel electrophoresis (AGE), pulse field gel electrophoresis (PFGE), capillary gel electrophoresis (CGE), mass photometry (MP), anion-exchange chromatography (AEX), ion-pairing reversed-phase liquid chromatography (IP-RPLC), hydrophobic interaction chromatography (HIC), size-exclusion chromatography (SEC), hydrodynamic chromatography (HDC), highly converging flow ultra-filtration (HCF-UF), asymmetrical flow field-flow fractionation (AF4), mass spectrometry (MS), and atomic force microscopy (AFM)) for analyzing mixtures containing large nucleic acid biopolymers, while assessing their strengths and weaknesses. We then focus comprehensively on the SC technique, report on its past applications since its birth, and review in detail the history and evolution of the proposed retention mechanisms accounting for the observations made in SC. This includes and emphasizes the latest physico-chemical insights (shear rates in packed HPLC columns, entropic elasticity and relaxation of dsDNA, dsRNA, and mRNA biopolymers) governing the retention behavior of such biopolymers in SC. Finally, based on the recent advancements in understanding the fundamentals of retention in SC, we provide some perspectives and recent proof-of-concept for the analytical characterization by SC of large dsDNAs (plasmid digests, polymerase chain reaction (PCR) verification), the separation of supercoiled/circular and linear dsDNAs (plasmid linearization), the isolation and quantification of large dsRNAs impurities present in mRNA samples produced by IVT, and the differentiation between dsRNA conformers.

The conformational space of RNase P RNA in solution

RNA conformational diversity has fundamental biological roles1-5, but direct visualization of its full conformational space in solution has not been possible using traditional biophysical techniques. Using solution atomic force microscopy, a deep neural network and statistical analyses, we show that the ribonuclease P (RNase P) RNA adopts heterogeneous conformations consisting of a conformationally invariant core and highly flexible peripheral structural elements that sample a broad conformational space, with amplitudes as large as 20-60 Å in a multitude of directions, with very low net energy cost. Increasing Mg2+ drives compaction and enhances enzymatic activity, probably by narrowing the conformational space. Moreover, analyses of the correlations and anticorrelations between spatial flexibility and sequence conservation suggest that the functional roles of both the structure and dynamics of key regions are embedded in the primary sequence. These findings reveal the structure-dynamics basis for the embodiment of both enzymatic precision and substrate promiscuity in the RNA component of the RNase P. Mapping the conformational space of the RNase P RNA demonstrates a new general approach to studying RNA structure and dynamics.

A Simple Protocol for Visualization of RNA-Protein Complexes by Atomic Force Microscopy

Atomic force microscopy (AFM) has recently received increasing interest in molecular biology. This technique allows quick and reliable detection of biomolecules. However, studying RNA-protein complexes using AFM poses significant challenges. Here, we describe a simple and reliable method to visualize positively charged proteins bound to RNA that does not require metallic cations. This method allowed us to effectively detect and visualize Staufen-RNA complexes by height or logarithmic stiffness. The study of the mechanical properties is particularly important in the case of protein-coated RNA complexes, where RNA cannot be detected by height channel. In any case, it is necessary to compare AFM data with the data derived from other techniques like nuclear magnetic resonance, X-ray crystallography, cryogenic electron microscopy, and small-angle X-ray scattering. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Preparation and visualization of RNA-protein complex.

Determining structures of RNA conformers using AFM and deep neural networks

Much of the human genome is transcribed into RNAs1, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded2-are conformationally heterogeneous and flexible, which is a prerequisite for function3,4, but this limits the applicability of methods such as NMR, crystallography and cryo-electron microscopy for structure elucidation. Moreover, owing to the lack of a large RNA structure database, and no clear correlation between sequence and structure, approaches such as AlphaFold5 for protein structure prediction do not apply to RNA. Therefore, determining the structures of heterogeneous RNAs remains an unmet challenge. Here we report holistic RNA structure determination method using atomic force microscopy, unsupervised machine learning and deep neural networks (HORNET), a novel method for determining three-dimensional topological structures of RNA using atomic force microscopy images of individual molecules in solution. Owing to the high signal-to-noise ratio of atomic force microscopy, this method is ideal for capturing structures of large RNA molecules in distinct conformations. In addition to six benchmark cases, we demonstrate the utility of HORNET by determining multiple heterogeneous structures of RNase P RNA and the HIV-1 Rev response element (RRE) RNA. Thus, our method addresses one of the major challenges in determining heterogeneous structures of large and flexible RNA molecules, and contributes to the fundamental understanding of RNA structural biology.

Keeping up with a Quickly Diversifying Pharmaceutical Landscape

Small molecules and antibodies have dominated the pharmaceutical landscape for decades. However, limitations associated with therapeutic targets deemed "undruggable" and progress in biology and chemistry have led to the blossoming of drug modalities and therapeutic approaches. In 2023, a high number of 9 oligonucleotide and peptide products were approved by the Food and Drug Administration (FDA), accounting for 16% of all drugs approved. Additionally, for the first time, a clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 gene therapy product was approved for the treatment of sickle cell disease. New drug modalities possess a wide range of physicochemical properties and structures, which complicates their analytical characterization. Impurities are formed at each step of the oligonucleotide and peptide solid phase synthesis and during shelf life. Longer chain lengths lead to a higher number of closely related impurities that become increasingly more difficult to separate from the full-length product. Chemical modifications such as phosphorothioates (PS) result in the presence of diastereomers, which often require orthogonal methods for their profiling and strategies to prevent their interference with the separation of achiral impurities. In-vitro produced mRNA and plasmid DNA also present a variety of quality attributes that need to be determined, such as the polyA tail length or capping efficiency. Analytical challenges arise from the variety of drug modality physiochemical properties and attributes, fast turnaround times, and heightened level of characterization needed to enable data-driven decisions early in the drug development process. This perspective provides the author's views on the lessons learned and strategies employed in recent years.

RNA encodes physical information

Most amino acids are encoded by multiple codons, making the genetic code degenerate. Synonymous mutations affect protein translation and folding, but their impact on RNA itself is often neglected. We developed a genetic algorithm that introduces synonymous mutations to control the diversity of structures sampled by an mRNA. The behavior of the designed mRNAs reveals a physical code layered in the genetic code. We find that mRNA conformational heterogeneity directs physical properties and functional outputs of RNA-protein complexes and biomolecular condensates. The role of structure and disorder of proteins in biomolecular condensates is well appreciated, but we find that RNA conformational heterogeneity is equally important. This feature of RNA enables both evolution and engineers to build cellular structures with specific material and responsive properties.

Ultra-high pressure slalom chromatography: Application to the characterization of large DNA and RNA samples relevant in cell and gene therapy

Slalom chromatography (SC), initially co-discovered by Boyes and Kasai in the late 1980s, has recently re-emerged as a breakthrough technique to rapidly analyze DNA samples. With the development of cutting-edge ultra-high pressure liquid chromatography (UHPLC) systems and columns, SC now offers enhanced resolution and sensitivity for analyzing large DNA samples. By revisiting the fundamentals of the SC retention mechanism (non-equilibrium separation mode) and considering the physicochemical properties of DNA biopolymers (contour length, extension under shear flow, relaxation time), we provide analytical chemists with a general strategy and framework for selecting the most relevant applications in the expanding field of cell and gene therapy. We then present proof-of-concept data demonstrating the rapid separation (under 2 min) of plasmid digest samples containing linear double-stranded (ds) DNA macromolecules starting from 2 kbp to 25 kbp, as well as the accurate size determination (±6%) of linear dsDNAs. Additionally, we show rapid baseline separation and quantification of extensible linear dsDNAs, along with the more rigid plasmid dsDNA (supercoiled/circular/nicked circular). We also quantify dsRNA impurities present in vitro transcription (IVT) media used for producing new mRNA therapeutics and assess dsRNA structural heterogeneity (conformational isomers). These findings aim to support in a near future application chemists in addressing emerging bioanalytical challenges in cell and gene therapy by offering advanced SC columns and methods.

Nexus between RNA conformational dynamics and functional versatility

RNA conformational dynamics is pivotal for functional regulations in biology. RNA can function as versatile as protein but adopts multiple distinct structures. In this review, we provide a focused review of the recent advances in studies of RNA conformational dynamics and address some of the misconceptions about RNA structure and its conformational dynamics. We discuss why the traditional methods for structure determination come up short in describing RNA conformational space. The examples discussed provide illustrations of the structure-based mechanisms of RNAs with diverse roles, including viral, long noncoding, and catalytic RNAs, one of which focuses on the debated area of conformational heterogeneity of an RNA structural element in the HIV-1 genome.

Accurate RNA 3D structure prediction using a language model-based deep learning approach

Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to the scarcity of experimentally determined data, complicates computational prediction efforts. Here we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pretrained on ~23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate the superiority of RhoFold+ over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and interhelical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies.

Empowering the molecular ruler techniques with unnatural base pair system to explore conformational dynamics of flaviviral RNAs

RNA's inherent flexibility and dynamics pose great challenges to characterize its structure and dynamics using conventional techniques including X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy. Three complementary molecular ruler techniques, the electron paramagnetic resonance (EPR) spectroscopy, X-ray scattering interferometry (XSI) and Förster resonance energy transfer (FRET) which measure intramolecular and intermolecular pair-wise distance distributions in the nanometer range in a solution, have become increasingly popular and been widely used to explore RNA structure and dynamics. The prerequisites for successful application of such techniques are to achieve site-specific labeling of RNAs with spin labels, fluorescent tags, or gold nanoparticles, respectively, which are however, challenging, especially to large RNAs (generally >200 nts). Here, we briefly review the basics of these molecular rulers, how the NaM-TPT3 unnatural base pair system empower them, and their applications to explore conformational dynamics of large RNAs, especially in the context of flavivirus RNA genome.

Conformational penalties: New insights into nucleic acid recognition

The energy cost accompanying changes in the structures of nucleic acids when they bind partner molecules is a significant but underappreciated thermodynamic contribution to binding affinity and specificity. This review highlights recent advances in measuring conformational penalties and determining their contribution to the recognition, folding, and regulatory activities of nucleic acids. Notable progress includes methods for measuring and structurally characterizing lowly populated conformational states, obtaining ensemble information in high throughput, for large macromolecular assemblies, and in complex cellular environments. Additionally, quantitative and predictive thermodynamic models have been developed that relate conformational penalties to nucleic acid-protein association and cellular activity. These studies underscore the crucial role of conformational penalties in nucleic acid recognition.

RNA ensembles from in vitro to in vivo: Toward predictive models of RNA cellular function

Deepening our understanding of RNA biology and accelerating development of RNA-based therapeutics go hand-in-hand-both requiring a transition from qualitative descriptions of RNA structure to quantitative models capable of predicting RNA behaviors, and from a static to an ensemble view. Ensembles are determined from their free energy landscapes, which define the relative populations of conformational states and the energetic barriers separating them. Experimental determination of RNA ensembles over the past decade has led to powerful predictive models of RNA behavior in vitro. It has also been shown during this time that the cellular environment redistributes RNA ensembles, changing the abundances of functionally relevant conformers relative to in vitro contexts with subsequent functional RNA consequences. However, recent studies have demonstrated that testing models built from in vitro ensembles with highly quantitative measurements of RNA cellular function, aided by emerging computational methodologies, enables predictive modelling of cellular activity and biological discovery.

Theoretical predictions to facilitate the method development in slalom chromatography for the separation of large DNA molecules

Slalom chromatography (SC) re-emerged in 2024 due to the availability of low adsorption ultra-high pressure liquid chromatography (UHPLC) packed columns/instruments and large modalities being investigated in the context of cell and gene therapies. The physico-chemical principles of SC retention combined with hydrodynamic chromatography (HDC) exclusion have been recently reported. In SC, DNA macromolecules are retarded because: (1) they can be stretched to lengths comparable to the particle diameter, and (2) their elastic relaxation time is long enough to maintain them in non-equilibrium extended conformations under regular UHPLC shear flow conditions. Here, a quantitative HDC-SC retention model is consolidated. A general plate height model accounting for the band broadening of long DNA biopolymers along packed beds is also derived for supporting method development and predicting speed-resolution performance in SC. For illustration, the chromatographic speed-resolution properties in SC are predicted for the separation of specific critical pairs (4.0/4.5, 10/11, and 25/27 kbp) of linear dsDNA polymers. The calculations are performed for two available custom-made particle sizes, dp= 1.7 and 2.5μm, at a constant pressure of 10,000 psi. The predictions are directly validated from experimental data acquired using low adsorption MaxPeakTM 4.6 mm i.d. Columns packed with 1.7μm BEHTM 45 Å (15 cm long column) and 2.5μm BEH 125 Å (30 cm long column) Particles, and by injecting six linear dsDNAs (λ DNA-Hind III Digest). The LC system is very low dispersion ACQUITYTM UPLCTM I-class PLUS System, and the mobile phase is a 100 mM phosphate buffer at pH 8. Maximum resolution is always achieved when the average extended lengths of linear dsDNAs are equal to a critical length, which is proportional to the particle diameter and to the square root of the applied shear rate. Most advantageously, the experimental results reveal that the relaxation times of linear dsDNAs observed under shear flow conditions are two orders of magnitude shorter than those expected in the absence of flow: this enables the detection of the longest linear dsDNAs up to 25 kbp without irremediable loss in column performance. Finally, the retention-efficiency model elaborated in this work can be used to rapidly anticipate and develop methods (selection of particle size, column length, and operating pressure) for any targeted DNA and time-resolution constraints.

Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data

Recent advancements in RNA three-dimensional (3D) structure prediction have provided significant insights into RNA biology, highlighting the essential role of RNA in cellular functions and its therapeutic potential. This review summarizes the latest developments in computational methods, particularly the incorporation of artificial intelligence and machine learning, which have improved the efficiency and accuracy of RNA structure predictions. We also discuss the integration of new experimental data types, including cryoelectron microscopy (cryo-EM) techniques and high-throughput sequencing, which have transformed RNA structure modeling. The combination of experimental advances with computational methods represents a significant leap in RNA structure determination. We review the outcomes of RNA-Puzzles and critical assessment of structure prediction (CASP) challenges, which assess the state of the field and limitations of existing methods. Future perspectives are discussed, focusing on the impact of RNA 3D structure prediction on understanding RNA mechanisms and its implications for drug discovery and RNA-targeted therapies, opening new avenues in molecular biology.

The role of structure in regulatory RNA elements

Regulatory RNA elements fulfill functions such as translational regulation, control of transcript levels, and regulation of viral genome replication. Trans-acting factors (i.e., RNA-binding proteins) bind the so-called cis elements and confer functionality to the complex. The specificity during protein-RNA complex (RNP) formation often exploits the structural plasticity of RNA. Functional integrity of cis-trans pairs depends on the availability of properly folded RNA elements, and RNA conformational transitions can cause diseases. Knowledge of RNA structure and the conformational space is needed for understanding complex formation and deducing functional effects. However, structure determination of RNAs under in vivo conditions remains challenging. This review provides an overview of structured eukaryotic and viral RNA cis elements and discusses the effect of RNA structural equilibria on RNP formation. We showcase implications of RNA structural changes for diseases, outline strategies for RNA structure-based drug targeting, and summarize the methodological toolbox for deciphering RNA structures.

Beyond ligand binding: Single molecule observation reveals how riboswitches integrate multiple signals to balance bacterial gene regulation

Riboswitches are specialized RNA structures that orchestrate gene expression in response to sensing specific metabolite or ion ligands, mostly in bacteria. Upon ligand binding, these conformationally dynamic RNA motifs undergo structural changes that control critical gene expression processes such as transcription termination and translation initiation, thereby enabling cellular homeostasis and adaptation. Because RNA folds rapidly and co-transcriptionally, riboswitches make use of the low complexity of RNA sequences to adopt alternative, transient conformations on the heels of the transcribing RNA polymerase (RNAP), resulting in kinetic partitioning that defines the regulatory outcome. This review summarizes single molecule microscopy evidence that has begun to unveil a sophisticated network of dynamic, kinetically balanced interactions between riboswitch architecture and the gene expression machinery that, together, integrate diverse cellular signals.

Transcription start site choice regulates HIV-1 RNA conformation and function

HIV-1, the causative agent of AIDS, is a retrovirus that packages two copies of unspliced viral RNA as a dimer into newly budding virions. The unspliced viral RNA also serves as an mRNA template for translation of two polyproteins. Recent studies suggest that the fate of the viral RNA (genome or mRNA) is determined at the level of transcription. RNA polymerase II uses heterogeneous transcription start sites to generate major transcripts that differ in only two guanosines at the 5' end. Remarkably, this two-nucleotide difference is sufficient to alter the structure of the 5'-untranslated region and generate two pools of RNA with distinct functions. The presence of both RNA species is needed for optimal viral replication and fitness.

Challenges, advances, and opportunities in RNA structural biology by Cryo-EM

RNAs are remarkably versatile molecules that can fold into intricate three-dimensional (3D) structures to perform diverse cellular and viral functions. Despite their biological importance, relatively few RNA 3D structures have been solved, and our understanding of RNA structure-function relationships remains in its infancy. This limitation partly arises from challenges posed by RNA's complex conformational landscape, characterized by structural flexibility, formation of multiple states, and a propensity to misfold. Recently, cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for the visualization of conformationally dynamic RNA-only 3D structures. However, RNA's characteristics continue to pose challenges. We discuss experimental methods developed to overcome these hurdles, including the engineering of modular modifications that facilitate the visualization of small RNAs, improve particle alignment, and validate structural models.

Rate-Engineered Plasmon-Enhanced Fluorescence for Real-Time Microsecond Dynamics of Single Biomolecules

Single-molecule fluorescence has revealed a wealth of biochemical processes but does not give access to submillisecond dynamics involved in transient interactions and molecular dynamics. Here we overcome this bottleneck and demonstrate record-high photon count rates of >107 photons/s from single plasmon-enhanced fluorophores. This is achieved by combining two conceptual novelties: first, we balance the excitation and decay rate enhancements by the antenna's volume, resulting in maximum fluorescence intensity. Second, we enhance the triplet decay rate using a multicomponent surface chemistry that minimizes microsecond blinking. We demonstrate applications to two exemplary molecular processes: we first reveal transient encounters and hybridization of DNA with a 1 μs temporal resolution. Second, we exploit the field gradient around the nanoparticle as a molecular ruler to reveal microsecond intramolecular dynamics of multivalent complexes. Our results pave the way toward real-time microsecond studies of biochemical processes using an implementation compatible with existing single-molecule fluorescence methods.

Single cell spatial biology over developmental time can decipher pediatric brain pathologies

Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.

Assembly of SARS-CoV-2 nucleocapsid protein with nucleic acid

The viral genome of SARS-CoV-2 is packaged by the nucleocapsid (N-)protein into ribonucleoprotein particles (RNPs), 38 ± 10 of which are contained in each virion. Their architecture has remained unclear due to the pleomorphism of RNPs, the high flexibility of N-protein intrinsically disordered regions, and highly multivalent interactions between viral RNA and N-protein binding sites in both N-terminal (NTD) and C-terminal domain (CTD). Here we explore critical interaction motifs of RNPs by applying a combination of biophysical techniques to ancestral and mutant proteins binding different nucleic acids in an in vitro assay for RNP formation, and by examining nucleocapsid protein variants in a viral assembly assay. We find that nucleic acid-bound N-protein dimers oligomerize via a recently described protein-protein interface presented by a transient helix in its long disordered linker region between NTD and CTD. The resulting hexameric complexes are stabilized by multivalent protein-nucleic acid interactions that establish crosslinks between dimeric subunits. Assemblies are stabilized by the dimeric CTD of N-protein offering more than one binding site for stem-loop RNA. Our study suggests a model for RNP assembly where N-protein scaffolding at high density on viral RNA is followed by cooperative multimerization through protein-protein interactions in the disordered linker.

Conserved structures and dynamics in 5'-proximal regions of Betacoronavirus RNA genomes

Betacoronaviruses are a genus within the Coronaviridae family of RNA viruses. They are capable of infecting vertebrates and causing epidemics as well as global pandemics in humans. Mitigating the threat posed by Betacoronaviruses requires an understanding of their molecular diversity. The development of novel antivirals hinges on understanding the key regulatory elements within the viral RNA genomes, in particular the 5'-proximal region, which is pivotal for viral protein synthesis. Using a combination of cryo-electron microscopy, atomic force microscopy, chemical probing, and computational modeling, we determined the structures of 5'-proximal regions in RNA genomes of Betacoronaviruses from four subgenera: OC43-CoV, SARS-CoV-2, MERS-CoV, and Rousettus bat-CoV. We obtained cryo-electron microscopy maps and determined atomic-resolution models for the stem-loop-5 (SL5) region at the translation start site and found that despite low sequence similarity and variable length of the helical elements it exhibits a remarkable structural conservation. Atomic force microscopy imaging revealed a common domain organization and a dynamic arrangement of structural elements connected with flexible linkers across all four Betacoronavirus subgenera. Together, these results reveal common features of a critical regulatory region shared between different Betacoronavirus RNA genomes, which may allow targeting of these RNAs by broad-spectrum antiviral therapeutics.

Causes, functions, and therapeutic possibilities of RNA secondary structure ensembles and alternative states

RNA secondary structure plays essential roles in encoding RNA regulatory fate and function. Most RNAs populate ensembles of alternatively paired states and are continually unfolded and refolded by cellular processes. Measuring these structural ensembles and their contributions to cellular function has traditionally posed major challenges, but new methods and conceptual frameworks are beginning to fill this void. In this review, we provide a mechanism- and function-centric compendium of the roles of RNA secondary structural ensembles and minority states in regulating the RNA life cycle, from transcription to degradation. We further explore how dysregulation of RNA structural ensembles contributes to human disease and discuss the potential of drugging alternative RNA states to therapeutically modulate RNA activity. The emerging paradigm of RNA structural ensembles as central to RNA function provides a foundation for a deeper understanding of RNA biology and new therapeutic possibilities.

Cell phenotypes can be predicted from propensities of protein conformations

Proteins exist as dynamic conformational ensembles. Here we suggest that the propensities of the conformations can be predictors of cell function. The conformational states that the molecules preferentially visit can be viewed as phenotypic determinants, and their mutations work by altering the relative propensities, thus the cell phenotype. Our examples include (i) inactive state variants harboring cancer driver mutations that present active state-like conformational features, as in K-Ras4BG12V compared to other K-Ras4BG12X mutations; (ii) mutants of the same protein presenting vastly different phenotypic and clinical profiles: cancer and neurodevelopmental disorders; (iii) alterations in the occupancies of the conformational (sub)states influencing enzyme reactivity. Thus, protein conformational propensities can determine cell fate. They can also suggest the allosteric drugs efficiency.

Assembly reactions of SARS-CoV-2 nucleocapsid protein with nucleic acid

The viral genome of SARS-CoV-2 is packaged by the nucleocapsid (N-) protein into ribonucleoprotein particles (RNPs), 38±10 of which are contained in each virion. Their architecture has remained unclear due to the pleomorphism of RNPs, the high flexibility of N-protein intrinsically disordered regions, and highly multivalent interactions between viral RNA and N-protein binding sites in both N-terminal (NTD) and C-terminal domain (CTD). Here we explore critical interaction motifs of RNPs by applying a combination of biophysical techniques to mutant proteins binding different nucleic acids in an in vitro assay for RNP formation, and by examining mutant proteins in a viral assembly assay. We find that nucleic acid-bound N-protein dimers oligomerize via a recently described protein-protein interface presented by a transient helix in its long disordered linker region between NTD and CTD. The resulting hexameric complexes are stabilized by multi-valent protein-nucleic acid interactions that establish crosslinks between dimeric subunits. Assemblies are stabilized by the dimeric CTD of N-protein offering more than one binding site for stem-loop RNA. Our study suggests a model for RNP assembly where N-protein scaffolding at high density on viral RNA is followed by cooperative multimerization through protein-protein interactions in the disordered linker.

tRNA renovatio: Rebirth through fragmentation

tRNA function is based on unique structures that enable mRNA decoding using anticodon trinucleotides. These structures interact with specific aminoacyl-tRNA synthetases and ribosomes using 3D shape and sequence signatures. Beyond translation, tRNAs serve as versatile signaling molecules interacting with other RNAs and proteins. Through evolutionary processes, tRNA fragmentation emerges as not merely random degradation but an act of recreation, generating specific shorter molecules called tRNA-derived small RNAs (tsRNAs). These tsRNAs exploit their linear sequences and newly arranged 3D structures for unexpected biological functions, epitomizing the tRNA "renovatio" (from Latin, meaning renewal, renovation, and rebirth). Emerging methods to uncover full tRNA/tsRNA sequences and modifications, combined with techniques to study RNA structures and to integrate AI-powered predictions, will enable comprehensive investigations of tRNA fragmentation products and new interaction potentials in relation to their biological functions. We anticipate that these directions will herald a new era for understanding biological complexity and advancing pharmaceutical engineering.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Genetic dissection of regulation by a repressing and novel activating corrinoid riboswitch enables engineering of synthetic riboswitches

IMPORTANCE

In addition to proteins, microbes can use structured RNAs such as riboswitches for the important task of regulating gene expression. Riboswitches control gene expression by changing their structure in response to binding a small molecule and are widespread among bacteria. Here we determine the mechanism of regulation in a riboswitch that responds to corrinoids-a family of coenzymes related to vitamin B12. We report the alternative RNA secondary structures that couple corrinoid sensing with response in a repressing and novel activating corrinoid riboswitch. We then applied this knowledge to flipping the regulatory sign by constructing synthetic riboswitches that activate expression to a higher level than the natural one. In the process, we observed patterns in which sequence, in addition to structure, impacts function in paired RNA regions. The synthetic riboswitches we describe here have potential applications as biosensors.

Capturing heterogeneous conformers of cobalamin riboswitch by cryo-EM

RNA conformational heterogeneity often hampers its high-resolution structure determination, especially for large and flexible RNAs devoid of stabilizing proteins or ligands. The adenosylcobalamin riboswitch exhibits heterogeneous conformations under 1 mM Mg2+ concentration and ligand binding reduces conformational flexibility. Among all conformers, we determined one apo (5.3 Å) and four holo cryo-electron microscopy structures (overall 3.0-3.5 Å, binding pocket 2.9-3.2 Å). The holo dimers exhibit global motions of helical twisting and bending around the dimer interface. A backbone comparison of the apo and holo states reveals a large structural difference in the P6 extension position. The central strand of the binding pocket, junction 6/3, changes from an 'S'- to a 'U'-shaped conformation to accommodate ligand. Furthermore, the binding pocket can partially form under 1 mM Mg2+ and fully form under 10 mM Mg2+ within the bound-like structure in the absence of ligand. Our results not only demonstrate the stabilizing ligand-induced conformational changes in and around the binding pocket but may also provide further insight into the role of the P6 extension in ligand binding and selectivity.

Emerging functional principles of tRNA-derived small RNAs and other regulatory small RNAs

Recent advancements in small RNA sequencing have unveiled a previously hidden world of regulatory small noncoding RNAs (sncRNAs) that extend beyond the well-studied small interfering RNAs, microRNAs, and piwi-interacting RNAs. This exploration, starting with tRNA-derived small RNAs, has led to the discovery of a diverse universe of sncRNAs derived from various longer structured RNAs such as rRNAs, small nucleolar RNAs, small nuclear RNAs, Y RNAs, and vault RNAs, with exciting uncharted functional possibilities. In this perspective, we discuss the emerging functional principles of sncRNAs beyond the well-known RNAi-like mechanisms, focusing on those that operate independent of linear sequence complementarity but rather function in an aptamer-like fashion. Aptamers use 3D structure for specific interactions with ligands and are modulated by RNA modifications and subcellular environments. Given that aptamer-like sncRNA functions are widespread and present in species lacking RNAi, they may represent an ancient functional principle that predates RNAi. We propose a rethinking of the origin of RNAi and its relationship with these aptamer-like functions in sncRNAs and how these complementary mechanisms shape biological processes. Lastly, the aptamer-like function of sncRNAs highlights the need for caution in using small RNA mimics in research and therapeutics, as their specificity is not restricted solely to linear sequence.

Advances, Applications, and Perspectives in Small-Angle X-ray Scattering of RNA

RNAs exhibit a plethora of functions far beyond transmitting genetic information. Often, RNA functions are entailed in their structure, be it as a regulatory switch, protein binding site, or providing catalytic activity. Structural information is a prerequisite for a full understanding of RNA-regulatory mechanisms. Owing to the inherent dynamics, size, and instability of RNA, its structure determination remains challenging. Methods such as NMR spectroscopy, X-ray crystallography, and cryo-electron microscopy can provide high-resolution structures; however, their limitations make structure determination, even for small RNAs, cumbersome, if at all possible. Although at a low resolution, small-angle X-ray scattering (SAXS) has proven valuable in advancing structure determination of RNAs as a complementary method, which is also applicable to large-sized RNAs. Here, we review the technological and methodological advancements of RNA SAXS. We provide examples of the powerful inclusion of SAXS in structural biology and discuss possible future applications to large RNAs.

Crystal structure of Escherichia coli thiamine pyrophosphate-sensing riboswitch in the apo state

The thiamine pyrophosphate (TPP)-sensing riboswitch is one of the earliest discovered and most widespread riboswitches. Numerous structural studies have been reported for this riboswitch bound with various ligands. However, the ligand-free (apo) structure remains unknown. Here, we report a 3.1 Å resolution crystal structure of Escherichia coli TPP riboswitch in the apo state, which exhibits an extended, Y-shaped conformation further supported by small-angle X-ray scattering data and driven molecular dynamics simulations. The loss of ligand interactions results in helical uncoiling of P5 and disruption of the key tertiary interaction between the sensory domains. Opening of the aptamer propagates to the gene-regulatory P1 helix and generates the key conformational flexibility needed for the switching behavior. Much of the ligand-binding site at the three-way junction is unaltered, thereby maintaining a partially preformed pocket. Together, these results paint a dynamic picture of the ligand-induced conformational changes in TPP riboswitches that confer conditional gene regulation.

Genetic dissection of regulation by a repressing and novel activating corrinoid riboswitch enables engineering of synthetic riboswitches

The ability to sense and respond to intracellular metabolite levels enables cells to adapt to environmental conditions. Many prokaryotes use riboswitches - structured RNA elements usually located in the 5' untranslated region of mRNAs - to sense intracellular metabolites and respond by modulating gene expression. The corrinoid riboswitch class, which responds to adenosylcobalamin (coenzyme B12) and related metabolites, is among the most widespread in bacteria. The structural elements for corrinoid binding and the requirement for a kissing loop interaction between the aptamer and expression platform domains have been established for several corrinoid riboswitches. However, the conformational changes in the expression platform that modulate gene expression in response to corrinoid binding remain unknown. Here, we employ an in vivo GFP reporter system in Bacillus subtilis to define alternative secondary structures in the expression platform of a corrinoid riboswitch from Priestia megaterium by disrupting and restoring base-pairing interactions. Moreover, we report the discovery and characterization of the first riboswitch known to activate gene expression in response to corrinoids. In both cases, mutually exclusive RNA secondary structures are responsible for promoting or preventing the formation of an intrinsic transcription terminator in response to the corrinoid binding state of the aptamer domain. Knowledge of these regulatory mechanisms allowed us to develop synthetic corrinoid riboswitches that convert repressing riboswitches to riboswitches that robustly induce gene expression in response to corrinoids. Due to their high expression levels, low background, and over 100-fold level of induction, these synthetic riboswitches have potential use as biosensors or genetic tools.

Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks

The vast percentage of the human genome is transcribed into RNA, many of which contain various structural elements and are important for functions. RNA molecules are conformationally heterogeneous and functionally dyanmics1, even when they are structured and well-folded2, which limit the applicability of methods such as NMR, crystallography, or cryo-EM. Moreover, because of the lack of a large structure RNA database, and no clear correlation between sequence and structure, approaches like AlphaFold3 for protein structure prediction, do not apply to RNA. Therefore determining the structures of heterogeneous RNA is an unmet challenge. Here we report a novel method of determining RNA three-dimensional topological structures using deep neural networks and atomic force microscopy (AFM) images of individual RNA molecules in solution. Owing to the high signal-to-noise ratio of AFM, our method is ideal for capturing structures of individual conformationally heterogeneous RNA. We show that our method can determine 3D topological structures of any large folded RNA conformers, from ~ 200 to ~ 420 residues, the size range that most functional RNA structures or structural elements fall into. Thus our method addresses one of the major challenges in frontier RNA structural biology and may impact our fundamental understanding of RNA structure.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.