The human mitochondrial mRNA structurome reveals mechanisms of gene expression.

The human mitochondrial genome encodes crucial oxidative phosphorylation system proteins, pivotal for aerobic energy transduction. They are translated from nine monocistronic and two bicistronic transcripts whose native structures remain unexplored, posing a gap in understanding mitochondrial gene expression. In this work, we devised the mitochondrial dimethyl sulfate mutational profiling with sequencing (mitoDMS-MaPseq) method and applied detection of RNA folding ensembles using expectation-maximization (DREEM) clustering to unravel the native mitochondrial messenger RNA (mt-mRNA) structurome in wild-type (WT) and leucine-rich pentatricopeptide repeat-containing protein (LRPPRC)-deficient cells. Our findings elucidate LRPPRC's role as a holdase contributing to maintaining mt-mRNA folding and efficient translation. mt-mRNA structural insights in WT mitochondria, coupled with metabolic labeling, unveil potential mRNA-programmed translational pausing and a distinct programmed ribosomal frameshifting mechanism. Our data define a critical layer of mitochondrial gene expression regulation. These mt-mRNA folding maps provide a reference for studying mt-mRNA structures in diverse physiological and pathological contexts.

RNA Folding, Mutation, and Detection.

The structure of RNA molecules is absolutely critical to their functions in a biological system. RNA structure is dynamic and changes in response to cellular needs. Within the last few decades, there has been an increased interest in studying the structure of RNA molecules and how they change to support the needs of the cell in different conditions. Selective 2'-hydroxyl acylation-based mutational profiling using high-throughput sequencing is a powerful method to predict the secondary structure of RNA molecules both in vivo and in immunopurified samples. Selective 2'-hydroxyl acylation-based mutational profiling using high-throughput sequencing works by adding bulky groups onto accessible "flexible" bases in an RNA molecule that are not involved in any base-pairing or RNA-protein interactions. When the RNA is reverse transcribed into cDNA, the bulky groups are incorporated as base mutations, which can be compared to an unmodified control to identify the locations of flexible bases. The comparison of sequence data between modified and unmodified samples allows the computer software program (developed to generate reactivity profiles) to generate RNA secondary structure models. These models can be compared in a variety of conditions to determine how specific stimuli influence RNA secondary structures.

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models.

MOTIVATION: RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation. RESULTS: In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/ml4bio/RiboDiffusion.

Computational Design of Allosteric Ribozymes via Genetic Algorithms.

In vitro selection of allosteric ribozymes has many challenges, such as complex and time-consuming experimental procedures, uncertain results, and the unwanted functionality of the enriched sequences. The precise computational design of allosteric ribozymes is achievable using RNA secondary structure folding principles. The computational design of allosteric ribozymes is based on experimentally validated EAs, random search algorithms, and a partition function for RNA folding. The in silico design achieves an accuracy exceeding 90%. Various algorithms with different logic gates have been automated via computer programs that can quickly create many allosteric sequences. This can eliminate the need for in vitro selection of allosteric ribozymes, thus vastly reducing the time and cost required.

Enhancing CRISPR prime editing by reducing misfolded pegRNA interactions.

CRISPR prime editing (PE) requires a Cas9 nickase-reverse transcriptase fusion protein (known as PE2) and a prime editing guide RNA (pegRNA), an extended version of a standard guide RNA (gRNA) that both specifies the intended target genomic sequence and encodes the desired genetic edit. Here, we show that sequence complementarity between the 5' and the 3' regions of a pegRNA can negatively impact its ability to complex with Cas9, thereby potentially reducing PE efficiency. We demonstrate this limitation can be overcome by a simple pegRNA refolding procedure, which improved ribonucleoprotein-mediated PE efficiencies in zebrafish embryos by up to nearly 25-fold. Further gains in PE efficiencies of as much as sixfold could also be achieved by introducing point mutations designed to disrupt internal interactions within the pegRNA. Our work defines simple strategies that can be implemented to improve the efficiency of PE.

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction.

Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.

Structural Characterization of the Cotranscriptional Folding of the Thiamin Pyrophosphate Sensing thiC Riboswitch in Escherichia coli.

Riboswitches are RNA-regulating elements that mostly rely on structural changes to modulate gene expression at various levels. Recent studies have revealed that riboswitches may control several regulatory mechanisms cotranscriptionally, i.e., during the transcription elongation of the riboswitch or early in the coding region of the regulated gene. Here, we study the structure of the nascent thiamin pyrophosphate (TPP)-sensing thiC riboswitch in Escherichia coli by using biochemical and enzymatic conventional probing approaches. Our chemical (in-line and lead probing) and enzymatic (nucleases S1, A, T1, and RNase H) probing data provide a comprehensive model of how TPP binding modulates the structure of the thiC riboswitch. Furthermore, by using transcriptional roadblocks along the riboswitch sequence, we find that a certain portion of nascent RNA is needed to sense TPP that coincides with the formation of the P5 stem loop. Together, our data suggest that conventional techniques may readily be used to study cotranscriptional folding of nascent RNAs.

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

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.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2.

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

Cleaving Folded RNA by Multifunctional DNAzyme Nanomachines.

Both tight and specific binding of folded biological mRNA is required for gene silencing by oligonucleotide gene therapy agents. However, this is fundamentally impossible using the conventional oligonucleotide probes according to the affinity/specificity dilemma. This study addresses this problem for cleaving folded RNA by using multicomponent agents (dubbed 'DNA nanomachine' or DNM). DNMs bind RNA by four short RNA binding arms, which ensure tight and highly selective RNA binding. Along with the improved affinity, DNM maintain the high sequence selectivity of the conventional DNAzymes. DNM enabled up to 3-fold improvement in DNAzymes catalytic efficiency (kcat/Km) by facilitating both RNA substrate binding and product release steps of the catalytic cycle. This study demonstrates that multicomponent probes organized in sophisticated structures can help to achieve the balance between affinity and selectivity in recognizing folded RNA and thus creates a foundation for applying complex DNA nanostructures derived by DNA nanotechnology in gene therapy.

G-quadruplex folding in Xist RNA antagonizes PRC2 activity for stepwise regulation of X chromosome inactivation.

How Polycomb repressive complex 2 (PRC2) is regulated by RNA remains an unsolved problem. Although PRC2 binds G-tracts with the potential to form RNA G-quadruplexes (rG4s), whether rG4s fold extensively in vivo and whether PRC2 binds folded or unfolded rG4 are unknown. Using the X-inactivation model in mouse embryonic stem cells, here we identify multiple folded rG4s in Xist RNA and demonstrate that PRC2 preferentially binds folded rG4s. High-affinity rG4 binding inhibits PRC2's histone methyltransferase activity, and stabilizing rG4 in vivo antagonizes H3 at lysine 27 (H3K27me3) enrichment on the inactive X chromosome. Surprisingly, mutagenizing the rG4 does not affect PRC2 recruitment but promotes its release and catalytic activation on chromatin. H3K27me3 marks are misplaced, however, and gene silencing is compromised. Xist-PRC2 complexes become entrapped in the S1 chromosome compartment, precluding the required translocation into the S2 compartment. Thus, Xist rG4 folding controls PRC2 activity, H3K27me3 enrichment, and the stepwise regulation of chromosome-wide gene silencing.

Folding molecular origami from ribosomal RNA.

Approximately 80 percent of the total RNA in cells is ribosomal RNA (rRNA), making it an abundant and inexpensive natural source of long, single-stranded nucleic acid, which could be used as raw material for the fabrication of molecular origami. In this study, we demonstrate efficient and robust construction of 2D and 3D origami nanostructures utilizing cellular rRNA as a scaffold and DNA oligonucleotide staples. We present calibrated protocols for the robust folding of contiguous shapes from one or two rRNA subunits that are efficient to allow folding using crude extracts of total RNA. We also show that RNA maintains stability within the folded structure. Lastly, we present a novel and comprehensive analysis and insights into the stability of RNA:DNA origami nanostructures and demonstrate their enhanced stability when coated with polylysine-polyethylene glycol in different temperatures, low Mg2+ concentrations, human serum, and in the presence of nucleases (DNase I or RNase H). Thus, laying the foundation for their potential implementation in emerging biomedical applications, where folding rRNA into stable structures outside and inside cells would be desired.

Classified Dynamic Programming in RNA Structure Analysis.

Analysis of the folding space of RNA generally suffers from its exponential size. With classified Dynamic Programming algorithms, it is possible to alleviate this burden and to analyse the folding space of RNA in great depth. Key to classified DP is that the search space is partitioned into classes based on an on-the-fly computed feature. A class-wise evaluation is then used to compute class-wide properties, such as the lowest free energy structure for each class, or aggregate properties, such as the class' probability. In this paper we describe the well-known shape and hishape abstraction of RNA structures, their power to help better understand RNA function and related methods that are based on these abstractions.

LocARNA 2.0: Versatile Simultaneous Alignment and Folding of RNAs.

Generating accurate alignments of non-coding RNA sequences is indispensable in the quest for understanding RNA function. Nevertheless, aligning RNAs remains a challenging computational task. In the twilight-zone of RNA sequences with low sequence similarity, sequence homologies and compatible, favorable (a priori unknown) structures can be inferred only in dependency of each other. Thus, simultaneous alignment and folding (SA&F) remains the gold-standard of comparative RNA analysis, even if this method is computationally highly demanding. This text introduces to the recent release 2.0 of the software package LocARNA, focusing on its practical application. The package enables versatile, fast and accurate analysis of multiple RNAs. For this purpose, it implements SA&F algorithms in a specific, lightweight flavor that makes them routinely applicable in large scale. Its high performance is achieved by combining ensemble-based sparsification of the structure space and banding strategies. Probabilistic banding strongly improves the performance of LocARNA 2.0 even over previous releases, while simplifying its effective use. Enabling flexible application to various use cases, LocARNA provides tools to globally and locally compare, cluster, and multiply aligned RNAs based on optimization and probabilistic variants of SA&F, which optionally integrate prior knowledge, expressible by anchor and structure constraints.

Assessing the Quality of Cotranscriptional Folding Simulations.

Structural changes in RNAs are an important contributor to controlling gene expression not only at the posttranscriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5' region of the primary transcript regulates the downstream functional unit - usually an ORF - through premature termination of transcription. Not only such elements occur naturally, but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labor-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach because of both the size of the molecules and the timescales of interest. Even at the simplified level of secondary structures, further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation. In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription-regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux® or as a platform-independent Docker® image including support for Apple macOS® and Microsoft Windows®.

A Guide to Computational Cotranscriptional Folding Featuring the SRP RNA.

Although RNA molecules are synthesized via transcription, little is known about the general impact of cotranscriptional folding in vivo. We present different computational approaches for the simulation of changing structure ensembles during transcription, including interpretations with respect to experimental data from literature. Specifically, we analyze different mutations of the E. coli SRP RNA, which has been studied comparatively well in previous literature, yet the details of which specific metastable structures form as well as when they form are still under debate. Here, we combine thermodynamic and kinetic, deterministic, and stochastic models with automated and visual inspection of those systems to derive the most likely scenario of which substructures form at which point during transcription. The simulations do not only provide explanations for present experimental observations but also suggest previously unnoticed conformations that may be verified through future experimental studies.

ANNaMo: Coarse-grained modeling for folding and assembly of RNA and DNA systems.

The folding of RNA and DNA strands plays crucial roles in biological systems and bionanotechnology. However, studying these processes with high-resolution numerical models is beyond current computational capabilities due to the timescales and system sizes involved. In this article, we present a new coarse-grained model for investigating the folding dynamics of nucleic acids. Our model represents three nucleotides with a patchy particle and is parameterized using well-established nearest-neighbor models. Thanks to the reduction of degrees of freedom and to a bond-swapping mechanism, our model allows for simulations at timescales and length scales that are currently inaccessible to more detailed models. To validate the performance of our model, we conducted extensive simulations of various systems: We examined the thermodynamics of DNA hairpins, capturing their stability and structural transitions, the folding of an MMTV pseudoknot, which is a complex RNA structure involved in viral replication, and also explored the folding of an RNA tile containing a k-type pseudoknot. Finally, we evaluated the performance of the new model in reproducing the melting temperatures of oligomers and the dependence on the toehold length of the displacement rate in toehold-mediated displacement processes, a key reaction used in molecular computing. All in all, the successful reproduction of experimental data and favorable comparisons with existing coarse-grained models validate the effectiveness of the new model.

Multivalent DNAzyme agents for cleaving folded RNA.

Multivalent recognition and binding of biological molecules is a natural phenomenon that increases the binding stability (avidity) without decreasing the recognition specificity. In this study, we took advantage of this phenomenon to increase the efficiency and maintain high specificity of RNA cleavage by DNAzymes (Dz). We designed a series of DNA constructs containing two Dz agents, named here bivalent Dz devices (BDD). One BDD increased the cleavage efficiency of a folded RNA fragment up to 17-fold in comparison with the Dz of a conventional design. Such an increase was achieved due to both the improved RNA binding and the increased probability of RNA cleavage by the two catalytic cores. By moderating the degree of Dz agent association in BDD, we achieved excellent selectivity in differentiating single-base mismatched RNA, while maintaining relatively high cleavage rates. Furthermore, a trivalent Dz demonstrated an even greater efficiency than the BDD in cleaving folded RNA. The data suggests that the cooperative action of several RNA-cleaving units can significantly improve the efficiency and maintain high specificity of RNA cleavage, which is important for the development of Dz-based gene knockdown agents.