Alternate RNA Structures

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data1. Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function2-4. We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB)5. GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which overlapping triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

Kinetic pathway of HIV-1 TAR cotranscriptional folding

The Trans-Activator Receptor (TAR) RNA, located at the 5'-end untranslated region (5' UTR) of the human immunodeficiency virus type 1 (HIV-1), is pivotal in the virus's life cycle. As the initial functional domain, it folds during the transcription of viral mRNA. Although TAR's role in recruiting the Tat protein for trans-activation is established, the detailed kinetic mechanisms at play during early transcription, especially at points of temporary transcriptional pausing, remain elusive. Moreover, the precise physical processes of transcriptional pause and subsequent escape are not fully elucidated. This study focuses on the folding kinetics of TAR and the biological implications by integrating computer simulations of RNA folding during transcription with nuclear magnetic resonance (NMR) spectroscopy data. The findings reveal insights into the folding mechanism of a non-native intermediate that triggers transcriptional pause, along with different folding pathways leading to transcriptional pause and readthrough. The profiling of the cotranscriptional folding pathway and identification of kinetic structural intermediates reveal a novel mechanism for viral transcriptional regulation, which could pave the way for new antiviral drug designs targeting kinetic cotranscriptional folding pathways in viral RNAs.

Probing RNA structure and dynamics using nanopore and next generation sequencing

It has become increasingly evident that the structures RNAs adopt are conformationally dynamic; the various structured states that RNAs sample govern their interactions with other nucleic acids, proteins, and ligands to regulate a myriad of biological processes. Although several biophysical approaches have been developed and used to study the dynamic landscape of structured RNAs, technical limitations have limited their application to all classes of RNA due to variable size and flexibility. Recent advances combining chemical probing experiments with next-generation- and direct sequencing have emerged as an alternative approach to exploring the conformational dynamics of RNA. In this review, we provide a methodological overview of the sequencing-based techniques used to study RNA conformational dynamics. We discuss how different techniques have enabled us to better understand the propensity of RNAs from a variety of different classes to sample multiple conformational states. Finally, we present examples of the ways these techniques have reshaped how we think about RNA structure.

Chloroplast Ribosome Biogenesis Factors

The formation of chloroplasts can be traced back to an ancient event in which a eukaryotic host cell containing mitochondria ingested a cyanobacterium. Since then, chloroplasts have retained many characteristics of their bacterial ancestor, including their transcription and translation machinery. In this review, recent research on the maturation of rRNA and ribosome assembly in chloroplasts is explored, along with their crucial role in plant survival and their implications for plant acclimation to changing environments. A comparison is made between the ribosome composition and auxiliary factors of ancient and modern chloroplasts, providing insights into the evolution of ribosome assembly factors. Although the chloroplast contains ancient proteins with conserved functions in ribosome assembly, newly evolved factors have also emerged to help plants acclimate to changes in their environment and internal signals. Overall, this review offers a comprehensive analysis of the molecular mechanisms underlying chloroplast ribosome assembly and highlights the importance of this process in plant survival, acclimation and adaptation.

The application of single-molecule optical tweezers to study disease-related structural dynamics in RNA

RNA, a dynamic and flexible molecule with intricate three-dimensional structures, has myriad functions in disease development. Traditional methods, such as X-ray crystallography and nuclear magnetic resonance, face limitations in capturing real-time, single-molecule dynamics crucial for understanding RNA function. This review explores the transformative potential of single-molecule force spectroscopy using optical tweezers, showcasing its capability to directly probe time-dependent structural rearrangements of individual RNA molecules. Optical tweezers offer versatility in exploring diverse conditions, with the potential to provide insights into how environmental changes, ligands and RNA-binding proteins impact RNA behaviour. By enabling real-time observations of large-scale structural dynamics, optical tweezers emerge as an invaluable tool for advancing our comprehension of RNA structure and function. Here, we showcase their application in elucidating the dynamics of RNA elements in virology, such as the pseudoknot governing ribosomal frameshifting in SARS-CoV-2.

Regulation of bacterial gene expression by non-coding RNA: It is all about time!

Commensal and pathogenic bacteria continuously evolve to survive in diverse ecological niches by efficiently coordinating gene expression levels in their ever-changing environments. Regulation through the RNA transcript itself offers a faster and more cost-effective way to adapt than protein-based mechanisms and can be leveraged for diagnostic or antimicrobial purposes. However, RNA can fold into numerous intricate, not always functional structures that both expand and obscure the plethora of roles that regulatory RNAs serve within the cell. Here, we review the current knowledge of bacterial non-coding RNAs in relation to their folding pathways and interactions. We posit that co-transcriptional folding of these transcripts ultimately dictates their downstream functions. Elucidating the spatiotemporal folding of non-coding RNAs during transcription therefore provides invaluable insights into bacterial pathogeneses and predictive disease diagnostics. Finally, we discuss the implications of co-transcriptional folding andapplications of RNAs for therapeutics and drug targets.

Predicting RNA structures and functions by artificial intelligence

RNA functions by interacting with its intended targets structurally. However, due to the dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally or predict computationally. Artificial intelligence (AI) has revolutionized many biomedical fields and has been progressively utilized to deduce RNA structures, target binding, and associated functionality. Integrating structural and target binding information could also help improve the robustness of AI-based RNA function prediction and RNA design. Given the rapid development of deep learning (DL) algorithms, AI will provide an unprecedented opportunity to elucidate the sequence-structure-function relation of RNAs.

RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias

Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. As a step forward in tackling these issues, we extend to RNA an enhanced path-sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force promoting the generation of productive folding trajectories in an all-atom force field with explicit solvent. A rigorous variational principle is then applied to minimize the effect of the bias. Here, we report on an application of this method to RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. By comparison with analog simulations performed on small proteins with similar size and architecture, we show that the RNA folding landscape is significantly more frustrated, even for relatively small chains with a simple topology. The predicted RNA folding mechanisms are found to be consistent with the available experiments and some of the existing coarse-grained models. Due to its computational performance, this scheme provides a promising platform to efficiently gather atomistic RNA folding trajectories, thus retain the information about the chemical composition of the sequence.

Probing the dynamic RNA structurome and its functions

RNA is a key regulator of almost every cellular process, and the structures adopted by RNA molecules are thought to be central to their functions. The recent fast-paced evolution of high-throughput sequencing-based RNA structure mapping methods has enabled the rapid in vivo structural interrogation of entire cellular transcriptomes. Collectively, these studies are shedding new light on the long underestimated complexity of the structural organization of the transcriptome - the RNA structurome. Moreover, recent analyses are challenging the view that the RNA structurome is a static entity by revealing how RNA molecules establish intricate networks of alternative intramolecular and intermolecular interactions and that these ensembles of RNA structures are dynamically regulated to finely tune RNA functions in living cells. This new understanding of how RNA can shape cell phenotypes has important implications for the development of RNA-targeted therapeutic strategies.

Zur and zinc increase expression of E. coli ribosomal protein L31 through RNA-mediated repression of the repressor L31p

Bacteria can adapt in response to numerous stress conditions. One such stress condition is zinc depletion. The zinc-sensing transcription factor Zur regulates the way numerous bacterial species respond to severe changes in zinc availability. Under zinc sufficient conditions, Zn-loaded Zur (Zn2-Zur) is well-known to repress transcription of genes encoding zinc uptake transporters and paralogues of a few ribosomal proteins. Here, we report the discovery and mechanistic basis for the ability of Zur to up-regulate expression of the ribosomal protein L31 in response to zinc in E. coli. Through genetic mutations and reporter gene assays, we find that Zur achieves the up-regulation of L31 through a double repression cascade by which Zur first represses the transcription of L31p, a zinc-lacking paralogue of L31, which in turn represses the translation of L31. Mutational analyses show that translational repression by L31p requires an RNA hairpin structure within the l31 mRNA and involves the N-terminus of the L31p protein. This work uncovers a new genetic network that allows bacteria to respond to host-induced nutrient limiting conditions through a sophisticated ribosomal protein switching mechanism.

Riboswitch-mediated regulation of riboflavin biosynthesis genes in prokaryotes

Prokaryotic organisms frequently use riboswitches to quantify intracellular metabolite concentration via high-affinity metabolite receptors. Riboswitches possess a metabolite-sensing system that controls gene regulation in a cis-acting fashion at the initiation of transcriptional/translational level by binding with a specific metabolite and controlling various biochemical pathways. Riboswitch binds with flavin mononucleotide (FMN), a phosphorylated form of riboflavin and controls gene expression involved in riboflavin biosynthesis and transport pathway. The first step of the riboflavin biosynthesis pathway is initiated by the conversion of guanine nucleotide triphosphate (GTP), which is an intermediate of the purine biosynthesis pathway. An alternative pentose phosphate pathway of riboflavin biosynthesis includes the enzymatic conversion of ribulose-5-phosphate into 3, 4 dihydroxy-2-butanone-4-phosphates by DHBP synthase. The product of ribAB interferes with both GTP cyclohydrolase II as well as DHBP synthase activities, which catalyze the cleavage of GTP and converts DHBP Ribu5P in the initial steps of both riboflavin biosynthesis branches. Riboswitches are located in the 5' untranslated region (5' UTR) of messenger RNAs and contain an aptamer domain (highly conserved in sequence) where metabolite binding leads to a conformational change in an aptamer domain, which modulate the regulation of gene expression located on bacterial mRNA. In this review, we focus on how riboswitch regulates the riboflavin biosynthesis pathway in Bacillus subtilis and Lactobacillus plantarum.

R-BIND 2.0: An Updated Database of Bioactive RNA-Targeting Small Molecules and Associated RNA Secondary Structures

Discoveries of RNA roles in cellular physiology and pathology are increasing the need for new tools that modulate the structure and function of these biomolecules, and small molecules are proving useful. In 2017, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND) and discovered distinguishing physicochemical properties of RNA-targeting ligands, leading us to propose the existence of an "RNA-privileged" chemical space. Biennial updates of the database and the establishment of a website platform (rbind.chem.duke.edu) have provided new insights and tools to design small molecules based on the analyzed physicochemical and spatial properties. In this report and R-BIND 2.0 update, we refined the curation approach and ligand classification system as well as conducted analyses of RNA structure elements for the first time to identify new targeting strategies. Specifically, we curated and analyzed RNA target structural motifs to determine the properties of small molecules that may confer selectivity for distinct RNA secondary and tertiary structures. Additionally, we collected sequences of target structures and incorporated an RNA structure search algorithm into the website that outputs small molecules targeting similar motifs without a priori secondary structure knowledge. Cheminformatic analyses revealed that, despite the 50% increase in small molecule library size, the distinguishing properties of R-BIND ligands remained significantly different from that of proteins and are therefore still relevant to RNA-targeted probe discovery. Combined, we expect these novel insights and website features to enable the rational design of RNA-targeted ligands and to serve as a resource and inspiration for a variety of scientists interested in RNA targeting.

Length-dependent motions of SARS-CoV-2 frameshifting RNA pseudoknot and alternative conformations suggest avenues for frameshifting suppression

Conserved SARS-CoV-2 RNA regions of critical biological functions define excellent targets for anti-viral therapeutics against Covid-19 variants. One such region is the frameshifting element (FSE), responsible for correct translation of viral polyproteins. Here, we analyze molecular-dynamics motions of three FSE conformations, discovered by graph-theory analysis, and associated mutants designed by graph-based inverse folding: two distinct 3-stem H-type pseudoknots and a 3-way junction. We find that the prevalent H-type pseudoknot in literature adopts ring-like conformations, which in combination with 5' end threading could promote ribosomal pausing. An inherent shape switch from "L" to linear that may help trigger the frameshifting is suppressed in our designed mutant. The alternative conformation trajectories suggest a stable intermediate structure with mixed stem interactions of all three conformations, pointing to a possible transition pathway during ribosomal translation. These observations provide new insights into anti-viral strategies and frameshifting mechanisms.

RNA structures in alternative splicing and back-splicing

Alternative splicing greatly expands the transcriptomic and proteomic diversities related to physiological and developmental processes in higher eukaryotes. Splicing of long noncoding RNAs, and back- and trans- splicing further expanded the regulatory repertoire of alternative splicing. RNA structures were shown to play an important role in regulating alternative splicing and back-splicing. Application of novel sequencing technologies made it possible to identify genome-wide RNA structures and interaction networks, which might provide new insights into RNA splicing regulation in vitro to in vivo. The emerging transcription-folding-splicing paradigm is changing our understanding of RNA alternative splicing regulation. Here, we review the insights into the roles and mechanisms of RNA structures in alternative splicing and back-splicing, as well as how disruption of these structures affects alternative splicing and then leads to human diseases. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Intrinsic Regulatory Role of RNA Structural Arrangement in Alternative Splicing Control

Alternative splicing is a highly sophisticated process, playing a significant role in posttranscriptional gene expression and underlying the diversity and complexity of organisms. Its regulation is multilayered, including an intrinsic role of RNA structural arrangement which undergoes time- and tissue-specific alterations. In this review, we describe the principles of RNA structural arrangement and briefly decipher its cis- and trans-acting cellular modulators which serve as crucial determinants of biological functionality of the RNA structure. Subsequently, we engage in a discussion about the RNA structure-mediated mechanisms of alternative splicing regulation. On one hand, the impairment of formation of optimal RNA structures may have critical consequences for the splicing outcome and further contribute to understanding the pathomechanism of severe disorders. On the other hand, the structural aspects of RNA became significant features taken into consideration in the endeavor of finding potential therapeutic treatments. Both aspects have been addressed by us emphasizing the importance of ongoing studies in both fields.