Sequence Design for RNA-RNA Interactions.

The design of RNA sequences with desired structural properties presents a challenging computational problem with promising applications in biotechnology and biomedicine. Most regulatory RNAs function by forming RNA-RNA interactions, e.g., in order to regulate mRNA expression. It is therefore natural to consider problems where a sequence is designed to form a desired RNA-RNA interaction and switch between structures upon binding. This contribution demonstrates the use of the Infrared framework to design interacting sequences. Specifically, we consider the regulation of the rpoS mRNA by the sRNA DsrA and design artificial 5 ' UTRs that place a downstream protein coding gene under control of DsrA. The design process is explained step by step in a Jupyter notebook, accompanied by Python code. The text discusses setting up design constraints for sampling sequences in Infrared, computing quality measures, constructing a suitable cost function, as well as the optimization procedure. We show that not only thermodynamic but also kinetic folding features can be relevant. Kinetics of interaction formation can be estimated efficiently using the RRIkinDP tool, and the chapter explains how to include kinetic folding features from RRIkinDP directly in the cost function. The protocol implemented in our Jupyter notebook can easily be extended to consider additional requirements or adapted to novel design scenarios.

Complex In Silico RNA Design with MoiRNAiFold.

In the advent of the RNA therapeutics and diagnostics era, it is of great relevance to introduce new and more efficient RNA technologies that prove to be effective tools in practical contexts. Moreover, it is of utmost importance to develop and provide access to computational tools capable of designing such RNA constructs. Here we introduce one such novel diagnostics technology (Apta-SMART) and show how to design (using MoiRNAiFold) and implement it, step by step. Moreover, we show how to combine this technique with well-known RNA amplification methods and briefly mention some encouraging results.

Sequence Design Using RNAstructure.

RNA is present in all domains of life. It was once thought to be solely involved in protein expression, but recent advances have revealed its crucial role in catalysis and gene regulation through noncoding RNA. With a growing interest in exploring RNAs with specific structures, there is an increasing focus on designing RNA structures for in vivo and in vitro experimentation and for therapeutics. The development of RNA secondary structure prediction methods has also spurred the growth of RNA design software. However, there are challenges to designing RNA sequences that meet secondary structure requirements. One major challenge is that the secondary structure design problem is likely NP-hard, making it computationally intensive. Another issue is that objective functions need to consider the folding ensemble of RNA molecules to avoid off target structures. In this chapter, we provide protocols for two software tools from the RNAstructure package: "Design" for structured RNA sequence design and "orega" for unstructured RNA sequence design.

Machine Learning for RNA Design: LEARNA.

Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to remarkable results. In this chapter, we describe machine learning approaches specifically developed for the design of RNAs, with a focus on the learna_tools Python package, a collection of automated deep reinforcement learning algorithms for secondary structure-based RNA design. We explain the basic concepts of reinforcement learning and its extension, automated reinforcement learning, and outline how these concepts can be successfully applied to the design of RNAs. The chapter is structured to guide through the usage of the different programs with explicit examples, highlighting particular applications of the individual tools.

Simulated Annealing for RNA Design with SIMARD.

Ribonucleic acid (RNA) design is the inverse of RNA folding. RNA folding aims to identify the most likely secondary structure into which a given strand of nucleotides will fold. RNA design algorithms, on the other hand, attempt to design a strand of nucleotides that will fold into a specified secondary structure. Despite the apparent NP-hard nature of RNA design, promising results can be achieved when formulated as a combinatorial optimization problem and approached with simple heuristics. The main focus of this paper is to describe an RNA design algorithm based on simulated annealing. Additionally, noteworthy features and results will be presented herein.

3D-Based RNA Function Prediction Tools in rnaglib.

Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remain time-consuming and lack standardization. In this chapter, we describe the use of rnaglib, to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA 3D structures.

Monte Carlo Inverse RNA Folding.

The inverse RNA folding problem deals with designing a sequence of nucleotides that will fold into a desired target structure. Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. It learns a playout policy to intensify the search of the state space near the current best sequence. The algorithm uses a prior on the possible actions so as to perform non uniform playouts when learning the instance of problem at hand. We trained a transformer neural network on the inverse RNA folding problem using the Rfam database. This network is used to generate a prior for every Eterna100 puzzle. GNRPA is used with this prior to solve some of the instances of the Eterna100 dataset. The transformer prior gives better result than handcrafted heuristics.

SHAPE Probing to Screen Computationally Designed RNA.

RNA design is a major challenge for the future development of synthetic biology and RNA-based therapy. The development of efficient and accurate RNA design pipelines is based on trial and error strategies. The fast progression of such algorithms requires assaying the properties of many RNA sequences in a short time frame. High throughput RNA structure chemical probing technologies such as SHAPE-MaP allow for assaying RNA structure and interaction rapidly and at a very large scale. However, the promiscuity of the designed sequences that may differ only by one nucleotide requires special care. In addition, it necessitates the analysis and evaluation of many experimental results that may reveal to be very tedious. Here we propose an experimental and analytical workflow that eases the screening of thousands of designed RNA sequences at once. In particular, we have developed shapemap tools a customized software suite available at https://github.com/sargueil-citcom/shapemap-tools .

Datasets for Benchmarking RNA Design Algorithms.

RNA molecules play vital roles in many biological processes, such as gene regulation or protein synthesis. The adoption of a specific secondary and tertiary structure by RNA is essential to perform these diverse functions, making RNA a popular tool in bioengineering therapeutics. The field of RNA design responds to the need to develop novel RNA molecules that possess specific functional attributes. In recent years, computational tools for predicting RNA sequences with desired folding characteristics have improved and expanded. However, there is still a lack of well-defined and standardized datasets to assess these programs. Here, we present a large dataset of internal and multibranched loops extracted from PDB-deposited RNA structures that encompass a wide spectrum of design difficulties. Furthermore, we conducted benchmarking tests of widely utilized open-source RNA design algorithms employing this dataset.

Toward Increasing the Credibility of RNA Design.

In the problem of RNA design, also known as inverse folding, RNA sequences are predicted that achieve the desired secondary structure at the lowest possible free energy and under certain constraints. The designed sequences have applications in synthetic biology and RNA-based nanotechnologies. There are also known cases of the successful use of inverse folding to discover previously unknown noncoding RNAs. Several computational methods have been dedicated to the problem of RNA design. They differ by algorithm and additional parameters, e.g., those determining the goal function in the sequence optimization process. Users can obtain many promising RNA sequences quite easily. The more difficult issue is to critically evaluate them and select the most favorable and reliable sequence that form1s the expected RNA structure. The latter problem is addressed in this paper. We propose an RNA design protocol extended to include sequence evaluation, for which a 3D structure is used. Experiments show that the accuracy of RNA design can be improved by adding a 3D structure prediction and analysis step.

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies induced by secondary and tertiary structure, as well as mechanisms of structural flexibility, resulting in a model that can be successfully used for the design of allosteric RNA such as riboswitches. They have recently been experimentally validated as generative models for the SAM-I riboswitch aptamer domain sequence family. We introduce RBM mathematically and practically, providing self-contained code examples to download the necessary training sequence data, train the RBM, and sample novel sequences. We present in detail the implementation of algorithms necessary to use RBMs, focusing on applications in biological sequence modeling.

Mechanistic insights into how mixing factors govern polyelectrolyte-surfactant complexation in RNA lipid nanoparticle formulation.

HYPOTHESIS: Lipid nanoparticle self-assembly is a complex process that relies on ion pairing between nucleic acids and hydrophobic cationic lipid counterions for encapsulation. The chemical factors influencing this process, such as formulation composition, have been the focus of recent research. However, the physical factors, particularly the mixing protocol, which directly modulates these chemical factors, have yet to be mechanistically examined using a reproducible mixing platform comparable to the industry standard. We here utilize Flash NanoPrecipitation (FNP), a scalable rapid mixing platform, to isolate and systematically investigate how mixing factors influence this complexation step, first by using a model polyelectrolyte-surfactant system and then generalizing to a typical RNA lipid nanoparticle formulation. EXPERIMENTS: Aqueous polystyrene sulfonate (PSS) and cetrimonium bromide (CTAB) solutions are rapidly homogenized using reproducible FNP mixing and controlled flow rates at different stoichiometric ratios and total solids concentrations to form polyelectrolyte-surfactant complexes (PESCs). Then, key mixing factors such as total flow rate, inlet stream relative volumetric flow rate, and magnitude of flow fluctuation are studied using both this PESC system and an RNA lipid nanoparticle formulation. FINDINGS: Fluctuations in flow as low as ± 5 % of the total flow rate are found to severely compromise PESC formation. This result is replicated in the RNA lipid nanoparticle system, which exhibited significant differences in size (132.7 nm vs. 75.6 nm) and RNA encapsulation efficiency (34.0 % vs. 82.8 %) under fluctuating vs. steady flow. We explain these results in light of the chemical variables isolated and studied; slow or nonuniform mixing generates localized concentration gradients that disrupt the balance between the hydrophobic and electrostatic forces that drive complex formation. These experiments contribute to our understanding of the complexation stage of lipid nanoparticle formation and provide practical insights into the importance of developing controlled mixing protocols in industry.

Direct quantification of N6-methyladenosine fractions at specific site in RNA based on deoxyribozyme mediated CRISPR-Cas12a platform.

As the most abundant modification in eukaryotic messenger RNA (mRNA) and long noncoding RNA (lncRA), N6-methyladenosine (m6A) has been shown to play essential roles in various significant biological processes and attracted growing attention in recent years. To investigate its functions and dynamics, there is a critical need to quantitatively determine the m6A modification fractions at a precise location. Here, we report a deoxyribozyme mediated CRISPR-Cas12a platform (termed "DCAS") that can directly quantify m6A fractions at single-base resolution. DCAS employs a deoxyribozyme (VMC10) to selectively cleave the unmodified adenine (A) in the RNA, allowing only m6A-modified RNA amplified by RT-PCR. Leveraging the CRISPR-Cas12a quantify the PCR amplification products, DCAS can directly determine the presence of m6A at target sites and its fractions. The combination of CRISPR-Cas12a with RT-PCR has greatly improved the sensitivity and accuracy, enabling the detection of m6A-modified RNA as low as 100 aM in 2 fM total target RNA. This robustly represents an improvement of 2-3 orders of magnitude of sensitivity and selectivity compared to traditional standard methods, such as SCARLET and primer extension methods. Therefore, this method can be successfully employed to accurately determine m6A fractions in real biological samples, even in low abundance RNA biomarkers.

gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.

Characterization of the Conformational Hotspots of the RNA-Dependent RNA Polymerase Complex Identifies a Unique Structural Malleability of nsp8.

Several antiviral therapeutic approaches have been targeted toward the RNA-dependent RNA polymerase (RdRp) complex that is involved in viral genome replication. In SARS-CoV-2, although the RdRp is a multiprotein complex, the focus has been on the ligand binding catalytic core (nonstructural protein nsp12), and not the multiprotein functional dynamics. In this study, we focus on the conformational ensembles of the RdRp complex and their modulation by the presence of RNA, performing comprehensive microsecond-scale atomistic simulations of the apo- and RNA-bound complex. We delineate the differential impact of RNA on the constituent proteins, such as conformational polymorphisms, dominant segment-specific fluctuations, and the switch in dynamical crosstalk within the complex. We distinguish dynamical signatures of nsp7, nsp8, and nsp12 in the apo-state that are reduced in the presence of the RNA and appear to "prime" the complex for activity. Importantly, we identify a unique structural malleability of the nsp8 protein with high conformational heterogeneity in the apo state, especially at three sites (Y71 for nsp8A, and D52 and A66 for nsp8B). Our work highlights the functional implications of the polymorphism of nsp8 structures and reveals possibilities for the development of allosteric inhibitors.

Nucleo-SAFARI: Automated Identification of Fragment Ions in Top-Down MS/MS Spectra of Nucleic Acids.

Recent progress in top-down mass spectrometry analysis of progressively larger nucleic acids has enabled in-depth characterization of intact, modified RNA molecules. Development of methods for desalting and MS/MS fragmentation allows rapid acquisition of high-quality top-down MS/MS spectra of nucleic acids up to 100 nt, which has spurred the need for development of software approaches to identify and validate nucleic acid fragment ions. We have implemented an R-based approach to aid in analysis of MS/MS spectra of nucleic acids based on fragment ions observed directly in the m/z domain. This program, entitled Shiny Application for Fragment Assignment by Relative Isotopes (Nucleo-SAFARI), utilizes the Shiny HTML framework for deployment of a user-friendly application for automated annotation of top-down MS/MS spectra of nucleic acids recorded on Orbitrap mass spectrometer platforms. This approach proceeds through in silico generation of fragment ions and their isotopic distributions, followed by algorithmic assessment of the experimental isotopic distributions. Nucleo-SAFARI is available for download at https://github.com/mblanzillotti/Nucleo-SAFARI.

RNAtango: Analysing and comparing RNA 3D structures via torsional angles.

RNA molecules, essential for viruses and living organisms, derive their pivotal functions from intricate 3D structures. To understand these structures, one can analyze torsion and pseudo-torsion angles, which describe rotations around bonds, whether real or virtual, thus capturing the RNA conformational flexibility. Such an analysis has been made possible by RNAtango, a web server introduced in this paper, that provides a trigonometric perspective on RNA 3D structures, giving insights into the variability of examined models and their alignment with reference targets. RNAtango offers comprehensive tools for calculating torsion and pseudo-torsion angles, generating angle statistics, comparing RNA structures based on backbone torsions, and assessing local and global structural similarities using trigonometric functions and angle measures. The system operates in three scenarios: single model analysis, model-versus-target comparison, and model-versus-model comparison, with results output in text and graphical formats. Compatible with all modern web browsers, RNAtango is accessible freely along with the source code. It supports researchers in accurately assessing structural similarities, which contributes to the precision and efficiency of RNA modeling.

Origin & influence of autocatalytic reaction networks at the advent of the RNA world.

Research on the origin of life investigates the transition from abiotic chemistry to the emergence of biology, with the 'RNA world hypothesis' as the leading theory. RNA's dual role in storage and catalysis suggests its importance in this narrative. The discovery of natural ribozymes emphasizes RNA's catalytic capabilities in prebiotic environments, supporting the plausibility of an RNA world and prompting exploration of precellular evolution. Collective autocatalytic sets (CASs) mark a crucial milestone in this transition, fostering complexity through autocatalysis. While modern biology emphasizes sequence-specific polymerases, remnants of CASs persist in primary metabolism highlighting their significance. Autocatalysis, driven by CASs, promotes complexity through mutually interdependent catalytic sets. Yet, the transition from ribonucleotides to complex RNA oligomers remains puzzling. Questions persist about the genesis of the first self-replicating RNA molecule, RNA's stability in prebiotic conditions, and the shift to complex molecular reproduction. This review delves into diverse facets of the RNA world's emergence, addressing critical bottlenecks and scientific advances. Integrating insights from simulation and in vitro evolution research, we illuminate the multistep biogenesis of catalytic RNA from the abiotic world. Through this exploration, we aim to elucidate the journey from the primordial soup to the dawn of life, emphasizing the interplay between chemistry and biology in understanding life's origins.

Dynamic conformation: Marching toward circular RNA function and application.

Circular RNA is a group of covalently closed, single-stranded transcripts with unique biogenesis, stability, and conformation that play distinct roles in modulating cellular functions and also possess a great potential for developing circular RNA-based therapies. Importantly, due to its circular conformation, circular RNA generates distinct intramolecular base pairing that is different from the linear transcript. In this perspective, we review how circular RNA conformation can affect its turnover and modes of action, as well as what factors can modulate circular RNA conformation. We also discuss how understanding circular RNA conformation can facilitate learning about their functions as well as the remaining technological issues to further address their conformation. These efforts will ultimately inform the design of circular RNA-based platforms for biomedical applications.

RNA-driven phase transitions in biomolecular condensates.

RNAs and RNA-binding proteins can undergo spontaneous or active condensation into phase-separated liquid-like droplets. These condensates are cellular hubs for various physiological processes, and their dysregulation leads to diseases. Although RNAs are core components of many cellular condensates, the underlying molecular determinants for the formation, regulation, and function of ribonucleoprotein condensates have largely been studied from a protein-centric perspective. Here, we highlight recent developments in ribonucleoprotein condensate biology with a particular emphasis on RNA-driven phase transitions. We also present emerging future directions that might shed light on the role of RNA condensates in spatiotemporal regulation of cellular processes and inspire bioengineering of RNA-based therapeutics.