Deep Learning in RNA Structure Studies

GINClus: RNA structural motif clustering using graph isomorphism network

Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming. Particularly, discovering new RNA structural motif families is a hard problem and still largely depends on manual analysis. In this paper, we proposed an RNA structural motif clustering tool, named GINClus, which uses a semi-supervised deep learning model to cluster RNA motif candidates (RNA loop regions) based on both base interaction and 3D structure similarities. GINClus converts base interactions and 3D structures of RNA motif candidates into graph representations and using graph isomorphism network (GIN) model in combination with K-means and hierarchical agglomerative clustering, GINClus clusters the RNA motif candidates based on their structural similarities. GINClus has a clustering accuracy of 87.88% for known internal loop motifs and 97.69% for known hairpin loop motifs. Using GINClus, we successfully clustered the motifs of the same families together and were able to find 927 new instances of Sarcin-ricin, Kink-turn, Tandem-shear, Hook-turn, E-loop, C-loop, T-loop, and GNRA loop motif families. We also identified 12 new RNA structural motif families with unique structure and base-pair interactions.

RNA research for drug discovery: Recent advances and critical insight

The field of RNA research has experienced significant changes and is now at the forefront of contemporary drug development. This narrative overview explores the scientific developments and historical turning points in RNA research, emphasising the field's critical significance in the development of novel therapeutics. Important discoveries like antisense oligonucleotides (ASOs), mRNA therapies, and RNA interference (RNAi) have created novel treatment options that can be targeted, such as the ground-breaking mRNA vaccinations against COVID-19. Advances in high-throughput sequencing, single-cell RNA sequencing, and epitranscriptomics have further unravelled the complexity of RNA biology, shedding light on the intricacies of gene regulation and cellular diversity. The integration of computational tools and bioinformatics has propelled the identification of RNA-based biomarkers and the development of RNA therapeutics. Despite significant progress, challenges such as RNA stability, delivery, and off-target effects persist, necessitating continuous innovation and ethical considerations. This review provides a critical insight into the current state and prospects of RNA research, emphasising its transformative potential in drug discovery. By examining the interplay between technological advancements and therapeutic applications, we underscore the promising horizon for RNA-based interventions in treating a myriad of diseases, marking a new era in precision medicine.

RNA Structure: Function and Application in Plant Biology

RNA orchestrates intricate structures that influence gene expression and protein production in all living organisms, with implications for fundamental biology, medicine, and agriculture. Although extensive research has been conducted on RNA biology, many regulatory mechanisms remain elusive due to the complex and dynamic nature of RNA structures and past technological limitations. Recent advancements in RNA structure technology have revolutionized plant RNA biology research. Here, we review cutting-edge technologies for studying RNA structures in plants and their functional significance in diverse biological processes. Additionally, we highlight the pivotal role of RNA structure in influencing plant growth, development, and responses to environmental stresses. We also discuss the potential evolutionary significance of RNA structure in natural adaptation and crop domestication. Finally, we propose leveraging RNA structure-mediated gene regulation as an innovative strategy to bolster plant resilience against climate change.

AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices

Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco-friendly power generation from mechanical motion. They harness mechanical energy while enabling self-sustaining sensing for self-powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI-driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real-time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low-power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI-enhanced TENG solutions.

RNA structure prediction using deep learning - A comprehensive review

In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led tremendous progress in this field, resulting in significant improvements in prediction accuracy. This comprehensive review aims to provide an overview of the diverse strategies employed in predicting RNA secondary structures, emphasizing deep learning methods. The article categorizes the discussion into three main dimensions: feature extraction methods, existing state-of-the-art learning model architectures, and prediction approaches. We present a comparative analysis of various techniques and models highlighting their strengths and weaknesses. Finally, we identify gaps in the literature, discuss current challenges, and suggest future approaches to enhance model performance and applicability in RNA structure prediction tasks. This review provides a deeper insight into the subject and paves the way for further progress in this dynamic intersection of life sciences and artificial intelligence.

How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological Descriptors

Identifying novel and functional RNA structures remains a significant challenge in RNA motif design and is crucial for developing RNA-based therapeutics. Here we introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. Specifically, we apply graph theory enumeration to generate all 110,667 possible 2D dual graphs for vertex numbers ranging from 2 to 9. Among them, only 0.11% (121 dual graphs) correspond to approximately 200,000 known RNA atomic fragments/substructures (collected in 2021) using the RNA-as-Graphs (RAG) mapping method. The remaining 99.89% of the dual graphs may be RNA-like or non-RNA-like. To determine which dual graphs in the 99.89% hypothetical set are more likely to be associated with RNA structures, we apply computational topology descriptors using the Persistent Spectral Graphs (PSG) method to characterize each graph using 19 PSG-based features and use clustering algorithms that partition all possible dual graphs into two clusters. The cluster with the higher percentage of known dual graphs for RNA is defined as the "RNA-like" cluster, while the other is considered as "non-RNA-like". The distance of each dual graph to the center of the RNA-like cluster represents the likelihood of it belonging to RNA structures. From validation, our PSG-based RNA-like cluster includes 97.3% of the 121 known RNA dual graphs, suggesting good performance. Furthermore, 46.017% of the hypothetical RNAs are predicted to be RNA-like. Among the top 15 graphs identified as high-likelihood candidates for novel RNA motifs, 4 were confirmed from the RNA dataset collected in 2022. Significantly, we observe that all the top 15 RNA-like dual graphs can be separated into multiple subgraphs, whereas the top 15 non-RNA-like dual graphs tend not to have any subgraphs (subgraphs preserve pseudoknots and junctions). Moreover, a significant topological difference between top RNA-like and non-RNA-like graphs is evident when comparing their topological features (e.g. Betti-0 and Betti-1 numbers). These findings provide valuable insights into the size of the RNA motif universe and RNA design strategies, offering a novel framework for predicting RNA graph topologies and guiding the discovery of novel RNA motifs, perhaps anti-viral therapeutics by subgraph assembly.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

Identification of RNA structures and their roles in RNA functions

The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.

Cleaving Folded RNA with DNAzyme Agents

Cleavage of biological mRNA by DNAzymes (Dz) has been proposed as a variation of oligonucleotide gene therapy (OGT). The design of Dz-based OGT agents includes computational prediction of two RNA-binding arms with low affinity (melting temperatures (Tm ) close to the reaction temperature of 37 °C) to avoid product inhibition and maintain high specificity. However, RNA cleavage might be limited by the RNA binding step especially if the RNA is folded in secondary structures. This calls for the need for two high-affinity RNA-binding arms. In this study, we optimized 10-23 Dz-based OGT agents for cleavage of three RNA targets with different folding energies under multiple turnover conditions in 2 mM Mg2+ at 37 °C. Unexpectedly, one optimized Dz had each RNA-binding arm with a Tm ≥60 °C, without suffering from product inhibition or low selectivity. This phenomenon was explained by the folding of the RNA cleavage products into stable secondary structures. This result suggests that Dz with long (high affinity) RNA-binding arms should not be excluded from the candidate pool for OGT agents. Rather, analysis of the cleavage products' folding should be included in Dz selection algorithms. The Dz optimization workflow should include testing with folded rather than linear RNA substrates.

Tailor made: the art of therapeutic mRNA design

mRNA medicine is a new and rapidly developing field in which the delivery of genetic information in the form of mRNA is used to direct therapeutic protein production in humans. This approach, which allows for the quick and efficient identification and optimization of drug candidates for both large populations and individual patients, has the potential to revolutionize the way we prevent and treat disease. A key feature of mRNA medicines is their high degree of designability, although the design choices involved are complex. Maximizing the production of therapeutic proteins from mRNA medicines requires a thorough understanding of how nucleotide sequence, nucleotide modification and RNA structure interplay to affect translational efficiency and mRNA stability. In this Review, we describe the principles that underlie the physical stability and biological activity of mRNA and emphasize their relevance to the myriad considerations that factor into therapeutic mRNA design.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Lessons from discovery of true ADAR RNA editing sites in a human cell line

BACKGROUND

Conversion or editing of adenosine (A) into inosine (I) catalyzed by specialized cellular enzymes represents one of the most common post-transcriptional RNA modifications with emerging connection to disease. A-to-I conversions can happen at specific sites and lead to increase in proteome diversity and changes in RNA stability, splicing, and regulation. Such sites can be detected as adenine-to-guanine sequence changes by next-generation RNA sequencing which resulted in millions reported sites from multiple genome-wide surveys. Nonetheless, the lack of extensive independent validation in such endeavors, which is critical considering the relatively high error rate of next-generation sequencing, leads to lingering questions about the validity of the current compendiums of the editing sites and conclusions based on them.

RESULTS

Strikingly, we found that the current analytical methods suffer from very high false positive rates and that a significant fraction of sites in the public databases cannot be validated. In this work, we present potential solutions to these problems and provide a comprehensive and extensively validated list of A-to-I editing sites in a human cancer cell line. Our findings demonstrate that most of true A-to-I editing sites in a human cancer cell line are located in the non-coding transcripts, the so-called RNA 'dark matter'. On the other hand, many ADAR editing events occurring in exons of human protein-coding mRNAs, including those that can recode the transcriptome, represent false positives and need to be interpreted with caution. Nonetheless, yet undiscovered authentic ADAR sites that increase the diversity of human proteome exist and warrant further identification.

CONCLUSIONS

Accurate identification of human ADAR sites remains a challenging problem, particularly for the sites in exons of protein-coding mRNAs. As a result, genome-wide surveys of ADAR editome must still be accompanied by extensive Sanger validation efforts. However, given the vast number of unknown human ADAR sites, there is a need for further developments of the analytical techniques, potentially those that are based on deep learning solutions, in order to provide a quick and reliable identification of the editome in any sample.

Long non-coding RNA-targeting therapeutics: discovery and development update

INTRODUCTION

lncRNAs are major players in regulatory networks orchestrating multiple cellular functions, such as 3D chromosomal interactions, epigenetic modifications, gene expression and others. Due to progress in the development of nucleic acid-based therapeutics, lncRNAs potentially represent easily accessible therapeutic targets.

AREAS COVERED

Currently, significant efforts are directed at studies that can tap the enormous therapeutic potential of lncRNAs. This review describes recent developments in this field, particularly focusing on clinical applications.

EXPERT OPINION

Extensive druggable target range of lncRNA combined with high specificity and accelerated development process of nucleic acid-based therapeutics open new prospects for treatment in areas of extreme unmet medical need, such as genetic diseases, aggressive cancers, protein deficiencies, and subsets of common diseases caused by known mutations. Although currently wide acceptance of lncRNA-targeting nucleic acid-based therapeutics is impeded by the need for parenteral or direct-to-CNS administration, development of less invasive techniques and orally available/BBB-penetrant nucleic acid-based therapeutics is showing early successes. Recently, mRNA-based COVID-19 vaccines have demonstrated clinical safety of all aspects of nucleic acid-based therapeutic technology, including multiple chemical modifications of nucleic acids and nanoparticle delivery. These trends position lncRNA-targeting drugs as significant players in the future of drug development, especially in the area of personalized medicine.

High-throughput biochemistry in RNA sequence space: predicting structure and function

RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.

G4Atlas: a comprehensive transcriptome-wide G-quadruplex database

RNA G-quadruplex (rG4) is a vital RNA tertiary structure motif that involves the base pairs on both Hoogsteen and Watson-Crick faces of guanines. rG4 is of great importance in the post-transcriptional regulation of gene expression. Experimental technologies have advanced to identify in vitro and in vivo rG4s across diverse transcriptomes. Building on these recent advances, here we present G4Atlas, the first transcriptome-wide G-quadruplex database, in which we have collated, classified, and visualized transcriptome rG4 experimental data, generated from rG4-seq, chemical profiling and ligand-binding methods. Our comprehensive database includes transcriptome-wide rG4s generated from 82 experimental treatments and 238 samples across ten species. In addition, we have also included RNA secondary structure prediction information across both experimentally identified and unidentified rG4s to enable users to display any potential competitive folding between rG4 and RNA secondary structures. As such, G4Atlas will enable users to explore the general functions of rG4s in diverse biological processes. In addition, G4Atlas lays the foundation for further data-driven deep learning algorithms to examine rG4 structural features.

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.