CENTROIDFOLD: a web server for RNA secondary structure prediction

Elimination of zero-repeat subunit in allergenic seed protein 13S globulin using the novel allele GlbNB2 in common buckwheat (Fagopyrum esculentum Moench)

Common buckwheat (Fagopyrum esculentum Moench) seeds contain 13S globulin, the zero-repeat subunit of which is trypsin-resistant and allergenic. Here, its two novel alleles were analyzed for development of hypoallergenic plants. The GlbNC allele has a Miniature Inverted-repeat Transposable Element (MITE)-like insertion in the 4th exon. However, most of the insertion was spliced-out, resulting in accumulation of zero-repeat subunit in GlbNC homozygotes. Meanwhile, the GlbNB2 has a 164-bp insertion in the 3rd exon, resulting in no accumulation of zero-repeat subunit in GlbNB2 homozygotes (NB2_homo). Both the insertion sequences were predicted to form a hairpin-like structure, and that of GlbNB2 was more rigid than that of GlbNC. Trypsin digestion in NB2_homo showed that the α polypeptide of Met-rich subunit is also hard to digest, that is a next target to eliminate for hypoallergenic buckwheat development.

Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1

Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of -1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

Intermolecular base-pairing interactions, a unique topology and exoribonuclease-resistant noncoding RNAs drive formation of viral chimeric RNAs in plants

In plants, exoribonuclease-resistant RNAs (xrRNAs) are produced by many viruses. Whereas xrRNAs contribute to the pathogenicity of these viruses, the role of xrRNAs in the virus infectious cycle remains elusive. Here, we show that xrRNAs produced by a benyvirus (a multipartite RNA virus with four genomic segments) in plants are involved in the formation of monocistronic coat protein (CP)-encoding chimeric RNAs. Naturally occurring chimeric RNAs, we discovered, are composed of 5'-end of RNA 2 and 3'-end of either RNA 3 or RNA 4 bearing conservative exoribonuclease-resistant 'coremin' region. Using computational tools and site-directed mutagenesis, we show that de novo formation of chimeric RNAs requires intermolecular base-pairing interaction between 'coremin' and 3'-proximal part of the CP gene of RNA 2 as well as a stem-loop structure immediately adjacent to the CP gene. Moreover, knockdown of the expression of the XRN4 gene, encoding 5'→3' exoribonuclease, inhibits biogenesis of both xrRNAs and chimeric RNAs. Our findings suggest a novel mechanism involving a unique tropology of the intermolecular base-pairing complex between xrRNAs and RNA2 to promote formation of chimeric RNAs in plants. XrRNAs, essential for chimeric RNA biogenesis, are generated through the action of cytoplasmic Xrn 4 5'→3' exoribonuclease conserved in all plant species.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.

RNA tertiary structure prediction using RNAComposer in CASP15

As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.

Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools

BACKGROUND

Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure.

RESULTS

The minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with [Formula: see text] of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only [Formula: see text] of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5-10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions.

CONCLUSIONS

In general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots.

Effect of single nucleotide polymorphisms on the structure of long noncoding RNAs and their interaction with RNA binding proteins

Long non-coding RNAs (lncRNA) are emerging as a new class of regulatory RNAs with remarkable potential to be utilized as therapeutic targets against many human diseases. Several genome-wide association studies (GWAS) have catalogued Single Nucleotide Polymorphisms (SNPs) present in the noncoding regions of the genome from where lncRNAs originate. In this study, we have selected 67 lncRNAs with GWAS-tagged SNPs and have also investigated their role in affecting the local secondary structures. Majority of the SNPs lead to changes in the secondary structure of lncRNAs to a different extent by altering the base pairing patterns. These structural changes in lncRNA are also manifested in form of alteration in the binding site for RNA binding proteins (RBPs) along with affecting their binding efficacies. Ultimately, these structural modifications may influence the transcriptional and post-transcriptional pathways of these RNAs, leading to the causation of diseases. Hence, it is important to understand the possible underlying mechanism of RBPs in association with GWAS-tagged SNPs in human diseases.

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models

RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Investigating the Prevalence of RNA-Binding Metabolic Enzymes in E. coli

An open research field in cellular regulation is the assumed crosstalk between RNAs, metabolic enzymes, and metabolites, also known as the REM hypothesis. High-throughput assays have produced extensive interactome data with metabolic enzymes frequently found as hits, but only a few examples have been biochemically validated, with deficits especially in prokaryotes. Therefore, we rationally selected nineteen Escherichia coli enzymes from such datasets and examined their ability to bind RNAs using two complementary methods, iCLIP and SELEX. Found interactions were validated by EMSA and other methods. For most of the candidates, we observed no RNA binding (12/19) or a rather unspecific binding (5/19). Two of the candidates, namely glutamate-5-kinase (ProB) and quinone oxidoreductase (QorA), displayed specific and previously unknown binding to distinct RNAs. We concentrated on the interaction of QorA to the mRNA of yffO, a grounded prophage gene, which could be validated by EMSA and MST. Because the physiological function of both partners is not known, the biological relevance of this interaction remains elusive. Furthermore, we found novel RNA targets for the MS2 phage coat protein that served us as control. Our results indicate that RNA binding of metabolic enzymes in procaryotes is less frequent than suggested by the results of high-throughput studies, but does occur.

miR-1306-3p directly activates P2X3 receptors in primary sensory neurons to induce visceral pain in rats

ABSTRACT

Mounting evidence indicates that microRNAs (miRNAs) play critical roles in various pathophysiological conditions and diseases, but the physiological roles of extracellular miRNAs on the disease-related ion channels remain largely unknown. Here, we showed that miR-1306-3p evoked action potentials and induced inward currents of the acutely isolated rat dorsal root ganglion (DRG) neurons. The miR-1306-3p-induced effects were significantly inhibited by A317491, a potent inhibitor of the P2X3 receptor (P2X3R), or disappeared after the knockdown of P2X3Rs in DRG neurons. We further identified R180, K315, and R52 as the miR-1306-3p interaction sites on the extracellular domain of P2X3Rs, which were distinct from the orthosteric ATP-binding sites. Intrathecal injection of miR-1306-3p produced visceral pain but not somatic pain in normal control rats. Conversely, intrathecal application of a miR-1306-3p antagomir and A317491 significantly alleviated visceral pain in a rat model of chronic visceral pain. Together, our findings suggest that miR-1306-3p might function as an endogenous ligand to activate P2X3Rs, eventually leading to chronic visceral pain.

Functional viromic screens uncover regulatory RNA elements

The number of sequenced viral genomes has surged recently, presenting an opportunity to understand viral diversity and uncover unknown regulatory mechanisms. Here, we conducted a screening of 30,367 viral segments from 143 species representing 96 genera and 37 families. Using a library of viral segments in 3' UTR, we identified hundreds of elements impacting RNA abundance, translation, and nucleocytoplasmic distribution. To illustrate the power of this approach, we investigated K5, an element conserved in kobuviruses, and found its potent ability to enhance mRNA stability and translation in various contexts, including adeno-associated viral vectors and synthetic mRNAs. Moreover, we identified a previously uncharacterized protein, ZCCHC2, as a critical host factor for K5. ZCCHC2 recruits the terminal nucleotidyl transferase TENT4 to elongate poly(A) tails with mixed sequences, delaying deadenylation. This study provides a unique resource for virus and RNA research and highlights the potential of the virosphere for biological discoveries.

Stress granules sequester Alzheimer's disease-associated gene transcripts and regulate disease-related neuronal proteostasis

Environmental and physiological stresses can accelerate Alzheimer's disease (AD) pathogenesis. Under stress, a cytoplasmic membraneless structure termed a stress granule (SG) is formed and is associated with various neurodegenerative disorders, including AD. SGs contain translationally arrested mRNAs, suggesting that impaired RNA metabolism in neurons causes AD progression; however, the underlying mechanism remains unclear. Here, we identified numerous mRNAs and long non-coding RNAs that are directly targeted by the SG core proteins G3BP1 and G3BP2. They redundantly target RNAs before and after stress conditions. We further identified RNAs within SGs, wherein AD-associated gene transcripts accumulated, suggesting that SGs can directly regulate AD development. Furthermore, gene-network analysis revealed a possible link between the sequestration of RNAs by SGs and the impairment of protein neurohomeostasis in AD brains. Together, our study provides a comprehensive RNA regulatory mechanism involving SGs, which could be targeted therapeutically to slow AD progression mediated by SGs.

Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction

Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.

Synthetic circular RNA switches and circuits that control protein expression in mammalian cells

Synthetic messenger RNA (mRNA) has been focused on as an emerging application for mRNA-based therapies and vaccinations. Recently, synthetic circular RNAs (circRNAs) have shown promise as a new class of synthetic mRNA that enables superior stability and persistent gene expression in cells. However, translational control of circRNA remained challenging. Here, we develop 'circRNA switches' capable of controlling protein expression from circRNA by sensing intracellular RNA or proteins. We designed microRNA (miRNA) and protein-responsive circRNA switches by inserting miRNA-binding or protein-binding sequences into untranslated regions (UTRs), or Coxsackievirus B3 Internal Ribosome Entry Site (CVB3 IRES), respectively. Engineered circRNAs efficiently expressed reporter proteins without inducing severe cell cytotoxicity and immunogenicity, and responded to target miRNAs or proteins, controlling translation levels from circRNA in a cell type-specific manner. Moreover, we constructed circRNA-based gene circuits that selectively activated translation by detecting endogenous miRNA, by connecting miRNA and protein-responsive circRNAs. The designed circRNA circuits performed better than the linear mRNA-based circuits in terms of persistent expression levels. Synthetic circRNA devices provide new insights into RNA engineering and have a potential for RNA synthetic biology and therapies.

Pseudoknots in RNA Structure Prediction

RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.

RNA Secondary Structure Prediction Based on Energy Models

This chapter introduces the RNA secondary structure prediction based on the nearest neighbor energy model, which is one of the most popular architectures of modeling RNA secondary structure without pseudoknots. We discuss the parameterization and the parameter determination by experimental and machine learning-based approaches as well as an integrated approach that compensates each other's shortcomings. Then, folding algorithms for the minimum free energy and the maximum expected accuracy using the dynamic programming technique are introduced. Finally, we compare the prediction accuracy of the method described so far with benchmark datasets.

Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies

INTRODUCTION

The field of RNA-targeted small molecules is rapidly evolving, owing to the advances in experimental and computational technologies. With the identification of several bioactive small molecules that target RNA, including the FDA-approved risdiplam, the biopharmaceutical industry is gaining confidence in the field. This review, based on the literature obtained from PubMed, aims to disseminate information about the various technologies developed for targeting RNA with small molecules and propose areas for improvement to develop drugs more efficiently, particularly those linked to diseases with unmet medical needs.

AREAS COVERED

The technologies for the identification of RNA targets, screening of chemical libraries against RNA, assessing the bioactivity and target engagement of the hit compounds, structure determination, and hit-to-lead optimization are reviewed. Along with the description of the technologies, their strengths, limitations, and examples of how they can impact drug discovery are provided.

EXPERT OPINION

Many existing technologies employed for protein targets have been repurposed for use in the discovery of RNA-targeted small molecules. In addition, technologies tailored for RNA targets have been developed. Nevertheless, more improvements are necessary, such as artificial intelligence to dissect important RNA structures and RNA-small-molecule interactions and more powerful chemical probing and structure prediction techniques.

Design and Prediction of Aptamers Assisted by In Silico Methods

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.

In silico and in vitro analysis of the impact of single substitutions within EXO-motifs on Hsa-MiR-1246 intercellular transfer in breast cancer cell

MiR-1246 has recently gained much attention and many studies have shown its oncogenic role in colorectal, breast, lung, and ovarian cancers. However, miR-1246 processing, stability, and mechanisms directing miR-1246 into neighbor cells remain still unclear. In this study, we aimed to determine the role of single-nucleotide substitutions within short exosome sorting motifs - so-called EXO-motifs: GGAG and GCAG present in miR-1246 sequence on its intracellular stability and extracellular transfer. We applied in silico methods such as 2D and 3D structure analysis and modeling of protein interactions. We also performed in vitro validation through the transfection of fluorescently labeled miRNA to MDA-MB-231 cells, which we analyzed by flow cytometry and fluorescent microscopy. Our results suggest that nucleotides alterations that disturbed miR-1246 EXO-motifs were able to modulate miRNA-1246 stability and its transfer level to the neighboring cells, suggesting that the molecular mechanism of RNA stability and intercellular transfer can be closely related.

AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures

MOTIVATION

Comparing single-stranded nucleic acids (ssNAs) secondary structures is fundamental when investigating their function and evolution and predicting the effect of mutations on their structures. Many comparison metrics exist, although they are either too elaborate or not sensitive enough to distinguish close ssNAs structures.

RESULTS

In this context, we developed AptaMat, a simple and sensitive algorithm for ssNAs secondary structures comparison based on matrices representing the ssNAs secondary structures and a metric built upon the Manhattan distance in the plane. We applied AptaMat to several examples and compared the results to those obtained by the most frequently used metrics, namely the Hamming distance and the RNAdistance, and by a recently developed image-based approach. We showed that AptaMat is able to discriminate between similar sequences, outperforming all the other here considered metrics. In addition, we showed that AptaMat was able to correctly classify 14 RFAM families within a clustering procedure.

AVAILABILITY AND IMPLEMENTATION

The python code for AptaMat is available at https://github.com/GEC-git/AptaMat.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico study predicts a key role of RNA-binding domains 3 and 4 in nucleolin-miRNA interactions

RNA binding proteins (RBPs) regulate many important cellular processes through their interactions with RNA molecules. RBPs are critical for posttranscriptional mechanisms keeping gene regulation in a fine equilibrium. Conversely, dysregulation of RBPs and RNA metabolism pathways is an established hallmark of tumorigenesis. Human nucleolin (NCL) is a multifunctional RBP that interacts with different types of RNA molecules, in part through its four RNA binding domains (RBDs). Particularly, NCL interacts directly with microRNAs (miRNAs) and is involved in their aberrant processing linked with many cancers, including breast cancer. Nonetheless, molecular details of the NCL-miRNA interaction remain obscure. In this study, we used an in silico approach to characterize how NCL targets miRNAs and whether this specificity is imposed by a definite RBD-interface. Here, we present structural models of NCL-RBDs and miRNAs, as well as predict scenarios of NCL-miRNA interactions generated using docking algorithms. Our study suggests a predominant role of NCL RBDs 3 and 4 (RBD3-4) in miRNA binding. We provide detailed analyses of specific motifs/residues at the NCL-substrate interface in both these RBDs and miRNAs. Finally, we propose that the evolutionary emergence of more than two RBDs in NCL in higher organisms coincides with its additional role/s in miRNA processing. Our study shows that RBD3-4 display sequence/structural determinants to specifically recognize miRNA precursor molecules. Moreover, the insights from this study can ultimately support the design of novel antineoplastic drugs aimed at regulating NCL-dependent biological pathways with a causal role in tumorigenesis.

Modulation of the Translation Efficiency of Heterologous mRNA and Target Protein Stability in a Plant System: The Case Study of Interferon-αA

A broad and amazingly intricate network of mechanisms underlying the decoding of a plant genome into the proteome forces the researcher to design new strategies to enhance both the accumulation of recombinant proteins and their purification from plants and to improve the available relevant strategies. In this paper, we propose new approaches to optimize a codon composition of target genes (case study of interferon-αA) and to search for regulatory sequences (case study of 5′UTR), and we demonstrated their effectiveness in increasing the synthesis of recombinant proteins in plant systems. In addition, we convincingly show that the approach utilizing stabilization of the protein product according to the N-end rule or a new protein-stabilizing partner (thermostable lichenase) is sufficiently effective and results in a significant increase in the protein yield manufactured in a plant system. Moreover, it is validly demonstrated that thermostable lichenase as a protein-stabilizing partner not only has no negative effect on the target protein activity (interferon-αA) integrated in its sequence, but rather enhances the accumulation of the target protein product in plant cells. In addition, the retention of lichenase enzyme activity and interferon biological activity after the incubation of plant protein lysates at 65 °C and precipitation of nontarget proteins with ethanol is applicable to a rapid and inexpensive purification of fusion proteins, thereby confirming the utility of thermostable lichenase as a protein-stabilizing partner for plant systems.

Cx43-mediated sorting of miRNAs into extracellular vesicles

Through the exchange of lipids, proteins, and nucleic acids, extracellular vesicles (EV) allow for cell-cell communication across distant cells and tissues to regulate a wide range of physiological and pathological processes. Although some molecular mediators have been discovered, the mechanisms underlying the selective sorting of miRNAs into EV remain elusive. Previous studies demonstrated that connexin43 (Cx43) forms functional channels at the EV surface, mediating the communication with recipient cells. Here, we show that Cx43 participates in the selective sorting of miRNAs into EV through a process that can also involve RNA-binding proteins. We provide evidence that Cx43 can directly bind to specific miRNAs, namely those containing stable secondary structure elements, including miR-133b. Furthermore, Cx43 facilitates the delivery of EV-miRNAs into recipient cells. Phenotypically, we show that Cx43-mediated EV-miRNAs sorting modulates autophagy. Overall, our study ascribes another biological role to Cx43, that is, the selective incorporation of miRNAs into EV, which potentially modulates multiple biological processes in target cells and may have implications for human health and disease.

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Background

G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies.

Results

G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R² of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans.

Conclusion

G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04782-z.

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

MOTIVATION

In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated.

RESULTS

In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches.

AVAILABILITY

Full source code and benchmark datasets are available at: https://github.com/sinc-lab/lncRNA-folding.

CONTACT

lbugnon@sinc.unl.edu.ar.

Dual role of CsrA in regulating the hemolytic activity of Escherichia coli O157:H7

Post-transcriptional global carbon storage regulator A (CsrA) is a sequence-specific RNA-binding protein involved in the regulation of multiple bacterial processes. Hemolysin is an important virulence factor in the enterohemorrhagic Escherichia coli O157:H7 (EHEC). Here, we show that CsrA plays a dual role in the regulation of hemolysis in EHEC. CsrA significantly represses plasmid-borne enterohemolysin (EhxA)-mediated hemolysis and activates chromosome-borne hemolysin E (HlyE)-mediated hemolysis through different mechanisms. RNA structure prediction revealed a well-matched stem-loop structure with two potential CsrA binding sites located on the 5' untranslated region (UTR) of ehxB, which encodes a translocator required for EhxA secretion. CsrA inhibits EhxA secretion by directly binding to the RNA leader sequence of ehxB to repress its expression in two different ways: CsrA either binds to the Shine–Dalgarno sequence of ehxB to block ribosome access or to ehxB transcript to promote its mRNA decay. The predicted CsrA-binding site 1 of ehxB is essential for its regulation. There is a single potential CsrA-binding site at the 5'-end of the hlyE transcript, and its mutation completely abolishes CsrA-dependent activation. CsrA can also stabilize hlyE mRNA by directly binding to its 5' UTR. Overall, our results indicate that CsrA acts as a hemolysis modulator to regulate pathogenicity under certain conditions.

A novel ceRNA-immunoregulatory axis based on immune cell infiltration in ulcerative colitis-associated colorectal carcinoma by integrated weighted gene co-expression network analysis

Background

Patients with ulcerative colitis are at an increased risk of developing colorectal cancer with a prolonged disease course. Many studies have shown that alterations in the immune microenvironment play a key role in ulcerative colitis-associated colorectal cancer. Additionally, competing endogenous RNAs have important functions in immunoregulation, affecting inflammation and tumorigenesis. However, the complexity and behavioral characteristics of the competing endogenous RNA immunoregulatory network in ulcerative colitis-associated colorectal cancer remain unclear. We constructed a competing endogenous RNA immunoregulatory network to discover and validate a novel competing endogenous RNA immunoregulatory axis to provide insight into ulcerative colitis-associated colorectal cancer progression.

Methods

The competing endogenous RNA immunoregulatory network was constructed using differential expression analysis, weighted gene co-expression network analysis, and immune-related genes. Cmap was used to identify small-molecule drugs with therapeutic potential in ulcerative colitis-associated colorectal cancer. The ulcerative colitis-associated colorectal cancer-related pathways were identified by gene set variation and enrichment analysis. CIBERSORT, single-sample Gene Set Enrichment Analysis, and xCell were used to evaluate the infiltration of immune cells and screen hub immunocytes. The competing endogenous RNA immunoregulatory axis was identified by correlation analysis.

Results

We identified 130 hub immune genes and constructed a competing endogenous RNA immunoregulatory network consisting of 56 long non-coding RNAs, four microRNAs, and six targeted hub immune genes. Four small-molecule drugs exerted potential therapeutic effects by reversing the expression of hub immune genes. Pathway analysis showed that the NF-κB pathway was significantly enriched. Neutrophils were identified as hub immunocytes, and IL6ST was significantly positively correlated with the neutrophil count. In addition, NEAT1 may serve as a competing endogenous RNA to sponge miR-1-3p and promote IL6ST expression.

Conclusions

The competing endogenous RNA immunoregulatory axis may regulate neutrophil infiltration, affecting the occurrence of ulcerative colitis-associated colorectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02252-7.

Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

Extended ensemble simulations of a SARS-CoV-2 nsp1-5'-UTR complex

Nonstructural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein that blocks translation of host mRNAs in SARS-CoV-2-infected cells. Although it is known that SARS-CoV-2’s own RNA evades nsp1’s host translation shutoff, the molecular mechanism underlying the evasion was poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results suggested that the stem loop structure of the SARS-CoV-2 RNA 5’-untranslated region (SL1) binds to both nsp1’s N-terminal globular region and intrinsically disordered region. The consistency of the results was assessed by modeling nsp1-40S ribosome structure based on reported nsp1 experiments, including the X-ray crystallographic structure analysis, the cryo-EM electron density map, and cross-linking experiments. The SL1 binding region predicted from the simulation was open to the solvent, yet the ribosome could interact with SL1. Cluster analysis of the binding mode and detailed analysis of the binding poses suggest residues Arg124, Lys47, Arg43, and Asn126 may be involved in the SL1 recognition mechanism, consistent with the existing mutational analysis.

The pandemic of COVID-19 is still rampant all over the world as of 2021 June. SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the causative pathogen of COVID-19, encodes a protein called nsp1 (nonstructural protein 1), which modulates and hijacks the ribosome of the infected host cells. With nsp1, infected human cells selectively translate SARS-CoV-2’s RNA, which increases the virus reproduction efficiency while evading the host immunity. Though it has been known that nsp1 recognizes characteristic stem-loop structure at 5’-end of SARS-CoV-2’s RNA (called SL1), the molecular mechanism underlying the recognition has been poorly understood. We investigated the mechanism of selective translation using the all-atom molecular dynamics simulation of nsp1-SL1 complex. Our simulation results suggest that the binding between nsp1 and SL1 is multi-modal. The results also imply that both the N-terminal globular part and the C-terminal flexible tail of nsp1 are involved in the binding. The residues involved in nsp1-SL1 binding coincides with the known mutant analyses of SARS-CoV-1 and SARS-CoV-2, as well as experimental evidence about nsp1-ribosome interactions.

Horizontal gene transfer as a mechanism for the promiscuous acquisition of distinct classes of IRES by avian caliciviruses

In contrast to members of Picornaviridae which have long 5'-untranslated regions (5'UTRs) containing internal ribosomal entry sites (IRESs) that form five distinct classes, members of Caliciviridae typically have short 5'UTRs and initiation of translation on them is mediated by interaction of the viral 5'-terminal genome-linked protein (VPg) with subunits of eIF4F rather than by an IRES. The recent description of calicivirus genomes with 500-900nt long 5'UTRs was therefore unexpected and prompted us to examine them in detail. Sequence analysis and structural modelling of the atypically long 5'UTRs of Caliciviridae sp. isolate yc-13 and six other caliciviruses suggested that they contain picornavirus-like type 2 IRESs, whereas ruddy turnstone calicivirus (RTCV) and Caliciviridae sp. isolate hwf182cal1 calicivirus contain type 4 and type 5 IRESs, respectively. The suggestion that initiation on RTCV mRNA occurs by the type 4 IRES mechanism was confirmed experimentally using in vitro reconstitution. The high sequence identity between identified calicivirus IRESs and specific picornavirus IRESs suggests a common evolutionary origin. These calicivirus IRESs occur in a single phylogenetic branch of Caliciviridae and were likely acquired by horizontal gene transfer.

Pheno-SELEX: Engineering Anti-Metastatic Aptamers through Targeting the Invasive Phenotype Using Systemic Evolution of Ligands by Exponential Enrichment

Multiple methods (e.g., small molecules and antibodies) have been engineered to target specific proteins and signaling pathways in cancer. However, many mediators of the cancer phenotype are unknown and the ability to target these phenotypes would help mitigate cancer. Aptamers are small DNA or RNA molecules that are designed for therapeutic use. The design of aptamers to target cancers can be challenging. Accordingly, to engineer functionally anti-metastatic aptamers we used a modification of systemic evolution of ligands by exponential enrichment (SELEX) we call Pheno-SELEX to target a known phenotype of cancer metastasis, i.e., invasion. A highly invasive prostate cancer (PCa) cell line was established and used to identify aptamers that bound to it with high affinity as opposed to a less invasive variant to the cell line. The anti-invasive aptamer (AIA1) was found to inhibit in vitro invasion of the original highly invasive PCa cell line, as well as an additional PCa cell line and an osteosarcoma cell line. AIA1 also inhibited in vivo development of metastasis in both a PCa and osteosarcoma model of metastasis. These results indicate that Pheno-SELEX can be successfully used to identify aptamers without knowledge of underlying molecular targets. This study establishes a new paradigm for the identification of functional aptamers.

Structural Requirements in the Hemagglutinin Cleavage Site-Coding RNA Region for the Generation of Highly Pathogenic Avian Influenza Virus

Highly pathogenic avian influenza viruses (HPAIVs) with H5 and H7 hemagglutinin (HA) subtypes are derived from their low pathogenic counterparts following the acquisition of multiple basic amino acids in their HA cleavage site. It has been suggested that consecutive adenine residues and a stem-loop structure in the viral RNA region that encodes the cleavage site are essential for the acquisition of the polybasic cleavage site. By using a reporter assay to detect non-templated nucleotide insertions, we found that insertions more frequently occurred in the RNA region (29 nucleotide-length) encoding the cleavage site of an H5 HA gene that was predicted to have a stem-loop structure containing consecutive adenines than in a mutated corresponding RNA region that had a disrupted loop structure with fewer adenines. In virus particles generated by using reverse genetics, nucleotide insertions that created additional codons for basic amino acids were found in the RNA region encoding the cleavage site of an H5 HA gene but not in the mutated RNA region. We confirmed the presence of virus clones with the ability to replicate without trypsin in a plaque assay and to cause lethal infection in chicks. These results demonstrate that the stem-loop structure containing consecutive adenines in HA genes is a key molecular determinant for the emergence of H5 HPAIVs.

UFold: fast and accurate RNA secondary structure prediction with deep learning

For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.

Modulation of MicroRNA Processing by Dicer via Its Associated dsRNA Binding Proteins

MicroRNAs (miRNAs) are small non-coding RNAs that are about 22 nucleotides in length. They regulate gene expression post-transcriptionally by guiding the effector protein Argonaute to its target mRNA in a sequence-dependent manner, causing the translational repression and destabilization of the target mRNAs. Both Drosha and Dicer, members of the RNase III family proteins, are essential components in the canonical miRNA biogenesis pathway. miRNA is transcribed into primary-miRNA (pri-miRNA) from genomic DNA. Drosha then cleaves the flanking regions of pri-miRNA into precursor-miRNA (pre-miRNA), while Dicer cleaves the loop region of the pre-miRNA to form a miRNA duplex. Although the role of Drosha and Dicer in miRNA maturation is well known, the modulation processes that are important for regulating the downstream gene network are not fully understood. In this review, we summarized and discussed current reports on miRNA biogenesis caused by Drosha and Dicer. We also discussed the modulation mechanisms regulated by double-stranded RNA binding proteins (dsRBPs) and the function and substrate specificity of dsRBPs, including the TAR RNA binding protein (TRBP) and the adenosine deaminase acting on RNA (ADAR).

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network

MOTIVATION

RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition.

RESULTS

Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs.

AVAILABILITY AND IMPLEMENTATION

Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Binding patterns of RNA-binding proteins to repeat-derived RNA sequences reveal putative functional RNA elements

Recent reports have revealed that repeat-derived sequences embedded in introns or long noncoding RNAs (lncRNAs) are targets of RNA-binding proteins (RBPs) and contribute to biological processes such as RNA splicing or transcriptional regulation. These findings suggest that repeat-derived RNAs are important as scaffolds of RBPs and functional elements. However, the overall functional sequences of the repeat-derived RNAs are not fully understood. Here, we show the putative functional repeat-derived RNAs by analyzing the binding patterns of RBPs based on ENCODE eCLIP data. We mapped all eCLIP reads to repeat sequences and observed that 10.75 % and 7.04 % of reads on average were enriched (at least 2-fold over control) in the repeats in K562 and HepG2 cells, respectively. Using these data, we predicted functional RNA elements on the sense and antisense strands of long interspersed element 1 (LINE1) sequences. Furthermore, we found several new sets of RBPs on fragments derived from other transposable element (TE) families. Some of these fragments show specific and stable secondary structures and are found to be inserted into the introns of genes or lncRNAs. These results suggest that the repeat-derived RNA sequences are strong candidates for the functional RNA elements of endogenous noncoding RNAs.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

First Complete Mitochondrial Genome of a Tanaidacean Crustacean (Arctotanais alascensis)

We present a complete mitochondrial genomic sequence for the tanaidacean Arctotanais alascensis (Richardson, 1899); this is the first complete mitogenome reported from the order Tanaidacea. The mitogenome is 13,988 bp long and contains 13 protein coding and two ribosomal RNA genes (as is typical for animal mitogenomes), and 21 of 22 transfer RNAs; we did not detect an isoleucine transfer RNA (trnI) gene. The gene order differed markedly from the hypothetical ground pattern for Pancrustacea; only four clusters (trnM + nad2; trnC + trnY + cox1 + trnL2 + cox2; trnD + atp8 + atp6 + cox3; trnH + nad4 + nad4l) ancestrally present were retained. In a malacostracan phylogenetic tree reconstructed from mitogenome data, basal relationships were marginally supported or incongruent with the traditional morphology-based classification and the latest phylogenetic reconstructions from large transcriptomic datasets. Relationships involving more recent divergences were better supported in our tree, suggesting that complete mitogenome sequences are more suitable for phylogenetic analyses within malacostracan orders, presumably including Tanaidacea.

Influence of the 5'-terminal sequences on the 5'-UTR structure of HIV-1 genomic RNA

The 5'-UTR of HIV-1 genomic RNA is known to form specific structures and has important functions. There are three 5'-terminal sequences, G1, G2 and G3, with different localizations in the cell and virion particles as well as different efficiencies in translation and reverse transcription reactions. In the present study, the structural characteristics of the joint region between the TAR and PolyA stems was analysed, and it was found that small differences in the 5'-terminus affect the conformational characteristics of the stem-loop structures. In the G1 form, the two stems form a coaxial stem, whereas in the G2 and G3 forms, the two stems are structurally independent of each other. In the case of the G1 form, the 3'-flanking nucleotides of the PolyA stem are included in the stable coaxial stem structure, which may affect the rest of the 5'-UTR structure. This result demonstrates that the local conformation of this functionally key region has an important role in the function of the 5'-UTR.

Artificial Intelligence in Aptamer-Target Binding Prediction

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer-target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer-target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

Improved RNA Secondary Structure and Tertiary Base-pairing Prediction Using Evolutionary Profile, Mutational Coupling and Two-dimensional Transfer Learning

MOTIVATION

The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling.

RESULTS

The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, noncanonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences.

AVAILABILITY

Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

RNA secondary structure prediction using deep learning with thermodynamic integration

Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.

Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.

Specific inhibition of FGF5-induced cell proliferation by RNA aptamers

Fibroblast growth factor 5 (FGF5) is a crucial regulator of hair growth and an oncogenic factor in several human cancers. To generate FGF5 inhibitors, we performed Systematic Evolution of Ligands by EXponential enrichment and obtained novel RNA aptamers that have high affinity to human FGF5. These aptamers inhibited FGF5-induced cell proliferation, but did not inhibit FGF2-induced cell proliferation. Surface plasmon resonance demonstrated that one of the aptamers, F5f1, binds to FGF5 tightly (K_d = 0.7 ± 0.2 nM), but did not fully to FGF1, FGF2, FGF4, FGF6, or FGFR1. Based on sequence and secondary structure similarities of the aptamers, we generated the truncated aptamer, F5f1_56, which has higher affinity (K_d = 0.118 ± 0.003 nM) than the original F5f1. Since the aptamers have high affinity and specificity to FGF5 and inhibit FGF5-induced cell proliferation, they may be candidates for therapeutic use with FGF5-related diseases or hair disorders.

Advances in the Bioinformatics Knowledge of mRNA Polyadenylation in Baculovirus Genes

Baculoviruses are a group of insect viruses with large circular dsDNA genomes exploited in numerous biotechnological applications, such as the biological control of agricultural pests, the expression of recombinant proteins or the gene delivery of therapeutic sequences in mammals, among others. Their genomes encode between 80 and 200 proteins, of which 38 are shared by all reported species. Thanks to multi-omic studies, there is remarkable information about the baculoviral proteome and the temporality in the virus gene expression. This allows some functional elements of the genome to be very well described, such as promoters and open reading frames. However, less information is available about the transcription termination signals and, consequently, there are still imprecisions about what are the limits of the transcriptional units present in the baculovirus genomes and how is the processing of the 3′ end of viral mRNA. Regarding to this, in this review we provide an update about the characteristics of DNA signals involved in this process and we contribute to their correct prediction through an exhaustive analysis that involves bibliography information, data mining, RNA structure and a comprehensive study of the core gene 3′ ends from 180 baculovirus genomes.

The Halastavi árva Virus Intergenic Region IRES Promotes Translation by the Simplest Possible Initiation Mechanism

Dicistrovirus intergenic region internal ribosomal entry sites (IGR IRESs) do not require initiator tRNA, an AUG codon, or initiation factors and jumpstart translation from the middle of the elongation cycle via formation of IRES/80S complexes resembling the pre-translocation state. eEF2 then translocates the [codon-anticodon]-mimicking pseudoknot I (PKI) from ribosomal A sites to P sites, bringing the first sense codon into the decoding center. Halastavi árva virus (HalV) contains an IGR that is related to previously described IGR IRESs but lacks domain 2, which enables these IRESs to bind to individual 40S ribosomal subunits. By using in vitro reconstitution and cryoelectron microscopy (cryo-EM), we now report that the HalV IGR IRES functions by the simplest initiation mechanism that involves binding to 80S ribosomes such that PKI is placed in the P site, so that the A site contains the first codon that is directly accessible for decoding without prior eEF2-mediated translocation of PKI.

Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.