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La-related protein 7 (LARP7) are a family of RNA chaperones that protect the 3'-end of RNA and are components of specific ribonucleoprotein complexes (RNP). In Tetrahymena thermophila telomerase, LARP7 protein p65 together with telomerase reverse transcriptase (TERT) and telomerase RNA (TER) form the core RNP. p65 has four known domains-N-terminal domain (NTD), La motif (LaM), RNA recognition motif 1 (RRM1), and C-terminal xRRM2. To date, only the xRRM2 and LaM and their interactions with TER have been structurally characterized. Conformational dynamics leading to low resolution in cryo-EM density maps have limited our understanding of how full-length p65 specifically recognizes and remodels TER for telomerase assembly. Here, we combined focused classification of Tetrahymena telomerase cryo-EM maps with NMR spectroscopy to determine the structure of p65-TER. Three previously unknown helices are identified, one in the otherwise intrinsically disordered NTD that binds the La module, one that extends RRM1, and another preceding xRRM2, that stabilize p65-TER interactions. The extended La module (αN, LaM and RRM1) interacts with the four 3' terminal U nucleotides, while LaM and αN additionally interact with TER pseudoknot, and LaM with stem 1 and 5' end. Our results reveal the extensive p65-TER interactions that promote TER 3'-end protection, TER folding, and core RNP assembly and stabilization. The structure of full-length p65 with TER also sheds light on the biological roles of genuine La and LARP7 proteins as RNA chaperones and core RNP components.

Long-range ribonucleic acid (RNA)–RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA–RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2's mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus–host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks. [ABSTRACT FROM AUTHOR]

The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance. [ABSTRACT FROM AUTHOR]

The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance.

Efficient and precise gene editing is the gold standard of any reverse genetic study. The recently developed prime editing approach, a modified CRISPR/Cas9 [clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein] editing method, has reached the precision goal but its editing rate can be improved. We present an improved methodology that allows for routine prime editing in the model plant Physcomitrium patens , whilst exploring potential new prime editing improvements. Using a standardized protoplast transfection procedure, multiple prime editing guide RNA (pegRNA) structural and prime editor variants were evaluated targeting the APT reporter gene through direct plant selection. Together, enhancements of expression of the prime editor, modifications of the 3ʹ extension of the pegRNA, and the addition of synonymous mutation in the reverse transcriptase template sequence of the pegRNA dramatically improve the editing rate without affecting the quality of the edits. Furthermore, we show that prime editing is amenable to edit a gene of interest through indirect selection, as demonstrated by the generation of a Ppdek1 0 mutant. Additionally, we determine that a plant retrotransposon reverse transcriptase enables prime editing. Finally, we show for the first time the possibility of performing prime editing with two independently coded peptides. [ABSTRACT FROM AUTHOR]

Abstract Background Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters. Results We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli’s performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT). Conclusions We found KnotAli’s performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli’s predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: https://doi.org/10.5281/zenodo.5794719

Non-B nucleic acids structures have arisen as key contributors to genetic variation in SARS-CoV-2. Herein, we investigated the presence of defining spike protein mutations falling within inverted repeats (IRs) for 18 SARS-CoV-2 variants, discussed the potential roles of G-quadruplexes (G4s) in SARS-CoV-2 biology, and identified potential pseudoknots within the SARS-CoV-2 genome. Surprisingly, there was a large variation in the number of defining spike protein mutations arising within IRs between variants and these were more likely to occur in the stem region of the predicted hairpin stem-loop secondary structure. Notably, mutations implicated in ACE2 binding and propagation (e.g., ΔH69/V70, N501Y, and D614G) were likely to occur within IRs, whilst mutations involved in antibody neutralization and reduced vaccine efficacy (e.g., T19R, ΔE156, ΔF157, R158G, and G446S) were rarely found within IRs. We also predicted that RNA pseudoknots could predominantly be found within, or next to, 29 mutations found in the SARS-CoV-2 spike protein. Finally, the Omicron variants BA.2, BA.4, BA.5, BA.2.12.1, and BA.2.75 appear to have lost two of the predicted G4-forming sequences found in other variants. These were found in nsp2 and the sequence complementary to the conserved stem-loop II-like motif (S2M) in the 3′ untranslated region (UTR). Taken together, non-B nucleic acids structures likely play an integral role in SARS-CoV-2 evolution and genetic diversity. [ABSTRACT FROM AUTHOR]

RNA molecules, in one form or another, are involved in almost all aspects of cell physiology, as well as in disease development. The diversity of the functional roles of RNA comes from its intrinsic ability to adopt complex secondary and tertiary structures, rivaling the diversity of proteins. The RNA molecules form dynamic ensembles of many interconverting conformations at a timescale of seconds, which is a key for understanding how they execute their cellular functions. Given the crucial role of RNAs in various cellular processes, we need to understand the RNA molecules from a structural perspective. Central to this review are studies aimed at revealing the regulatory role of conformational equilibria in RNA in humans to understand genetic diseases such as cancer and neurodegenerative diseases, as well as in pathogens such as bacteria and viruses so as to understand the progression of infectious diseases. Furthermore, we also summarize the prior studies on the use of RNA structures as platforms for the rational design of small molecules for therapeutic applications. [ABSTRACT FROM AUTHOR]

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Senecavirus A (SVA) is an emerging virus that belongs to the genus Senecavirus in the family Picornaviridae. Its genome is a positive-sense and single-stranded RNA, containing two untranslated regions (UTRs). The 68-nt-long 3′ UTR is computationally predicted to possess two higher-order RNA structures: a kissing-loop interaction and an H-type-like pseudoknot, both of which, however, cannot coexist in the 3′ UTR. In this study, we constructed 17 full-length SVA cDNA clones (cD-1 to -17): the cD-1 to -7 contained different point mutations in a kissing-loop-forming motif (KLFM); the cD-8 to -17 harbored one single or multiple point mutations in a pseudoknot-forming motif (PFM). These 17 mutated cDNA clones were independently transfected into BSR-T7/5 cells for rescuing recombinant SVAs (rSVAs), named rSVA-1 to −17, corresponding to cD-1 to −17. The results showed that the rSVA-1, -2, -3, -4, -5, -6, -7, -9, -13, and -15 were successfully rescued from their individual cDNA clones. Moreover, all mutated motifs were genetically stable during 10 viral passages in vitro. This study unveiled viral abilities of tolerating mutations in the computationally predicted KLFM or PFMs. It can be concluded that the putative kissing-loop structure, even if present in the 3′ UTR, is unnecessary for SVA replication. Alternatively, if the pseudoknot formation potentially occurs in the 3′ UTR, its deformation would have a lethal effect on SVA propagation. [ABSTRACT FROM AUTHOR]

Background: Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters. Results: We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli's performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT). Conclusions: We found KnotAli's performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli's predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: https://doi.org/10.5281/zenodo.5794719 [ABSTRACT FROM AUTHOR]

BackgroundIn this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms.ResultsWe have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools.ConclusionsWe have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo .

Senecavirus A (SVA) is an emerging virus that belongs to the genus Senecavirus in the family Picornaviridae. Its genome is a positive-sense and single-stranded RNA, containing two untranslated regions (UTRs). The 68-nt-long 3′ UTR is computationally predicted to possess two higher-order RNA structures: a kissing-loop interaction and an H-type-like pseudoknot, both of which, however, cannot coexist in the 3′ UTR. In this study, we constructed 17 full-length SVA cDNA clones (cD-1 to -17): the cD-1 to -7 contained different point mutations in a kissing-loop-forming motif (KLFM); the cD-8 to -17 harbored one single or multiple point mutations in a pseudoknot-forming motif (PFM). These 17 mutated cDNA clones were independently transfected into BSR-T7/5 cells for rescuing recombinant SVAs (rSVAs), named rSVA-1 to −17, corresponding to cD-1 to −17. The results showed that the rSVA-1, -2, -3, -4, -5, -6, -7, -9, -13, and -15 were successfully rescued from their individual cDNA clones. Moreover, all mutated motifs were genetically stable during 10 viral passages in vitro. This study unveiled viral abilities of tolerating mutations in the computationally predicted KLFM or PFMs. It can be concluded that the putative kissing-loop structure, even if present in the 3′ UTR, is unnecessary for SVA replication. Alternatively, if the pseudoknot formation potentially occurs in the 3′ UTR, its deformation would have a lethal effect on SVA propagation.

Hepatitis C virus (HCV) is a positive-strand RNA virus that remains one of the main contributors to chronic liver disease worldwide. Studies over the last 30 years have demonstrated that HCV contains a highly structured RNA genome and many of these structures play essential roles in the HCV life cycle. Despite the importance of riboregulation in this virus, most of the HCV RNA genome remains functionally unstudied. Here, we report a complete secondary structure map of the HCV RNA genome in vivo, which was studied in parallel with the secondary structure of the same RNA obtained in vitro. Our results show that HCV is folded extensively in the cellular context. By performing comprehensive structural analyses on both in vivo data and in vitro data, we identify compact and conserved secondary and tertiary structures throughout the genome. Genetic and evolutionary functional analyses demonstrate that many of these elements play important roles in the virus life cycle. In addition to providing a comprehensive map of RNA structures and riboregulatory elements in HCV, this work provides a resource for future studies aimed at identifying therapeutic targets and conducting further mechanistic studies on this important human pathogen. [ABSTRACT FROM AUTHOR]

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The 5′ untranslated region (UTR) of Senecavirus A (SVA) harbors an internal ribosome entry site (IRES), in which a pseudoknot structure is upstream of start codon AUG. Wild-type SVAs have a highly conserved 13-nt-sequence between the pseudoknot stem II (PKS-II)-forming motif and the AUG. In this study, a single nucleotide was deleted one by one from the 13-nt-sequence within a wild-type SVA minigenome. The result showed that neither mono- nor multi-nucleotide deletions abolished the IRES activity. Furthermore, a single nucleotide was deleted one by one from the 13-nt-sequence within a full-length SVA cDNA clone. The result indicated that nucleotide-deleting SVAs could be rescued from 1- to 5-nt-deleting cDNA clones, whereas only the 1- and 2-nt-deleting viruses were genetically stable during nine serial passages in vitro. Additionally, only the 1-nt-deleting SVA showed similar growth kinetics to that of the wild-type virus, suggesting that the pseudoknot-AUG distance was crucial for SVA replication. • SVA IRES activity cannot be abolished by deleting pseudoknot-AUG sequence (PAS). • SVAs can be rescued from cDNA clones with 1- to 5-nt-deleting PASs. • SVAs with 1- and 2-nt-deleting PASs are genetically stable during 9 passages. • SVA with 1-nt-deleting PAS has similar growth curve to that of wild-type virus. [ABSTRACT FROM AUTHOR]

Abstract Background RNA pseudoknot structures play an important role in biological processes. However, existing RNA secondary structure prediction algorithms cannot predict the pseudoknot structure efficiently. Although random matching can improve the number of base pairs, these non-consecutive base pairs cannot make contributions to reduce the free energy. Result In order to improve the efficiency of searching procedure, our algorithm take consecutive base pairs as the basic components. Firstly, our algorithm calculates and archive all the consecutive base pairs in triplet data structure, if the number of consecutive base pairs is greater than given minimum stem length. Secondly, the annealing schedule is adapted to select the optimal solution that has minimum free energy. Finally, the proposed algorithm is evaluated with the real instances in PseudoBase. Conclusion The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art RNA structure prediction algorithms.

Initial successes in developing small molecule ligands for non-coding RNAs have underscored their potential as therapeutic targets. More recently, these successes have been aided by advances in biophysical and structural techniques for identification and characterization of more complex RNA structures; these higher-level folds present protein-like binding pockets that offer opportunities to design small molecules that could achieve a degree of selectivity often hard to obtain at the primary and secondary structure level. More specifically, identification and small molecule targeting of RNA tertiary and quaternary structures have allowed researchers to probe several human diseases and have resulted in promising clinical candidates. In this review we highlight a selection of diverse and exciting successes and the experimental approaches that led to their discovery. These studies include examples of recent developments in RNA-centric assays and ligands that provide insight into the features responsible for the affinity and biological outcome of RNA-targeted chemical probes. This report highlights the potential and emerging opportunities to selectively target RNA tertiary and quaternary structures as a route to better understand and, ultimately, treat many diseases. [Display omitted] Zafferani et al. review diverse approaches that resulted in the successful small molecule targeting of RNA tertiary and quaternary structures. This review highlights the opportunities and challenges in developing chemical probes that target the exponentially growing number of functionally characterized RNA tertiary and quaternary structures across all domains of life. [ABSTRACT FROM AUTHOR]

Abstract Background RNAs can interact and form complexes, which have various biological roles. The secondary structure prediction of those complexes is a first step towards the identification of their 3D structure. We propose an original approach that takes advantage of the high number of RNA secondary structure and RNA-RNA interaction prediction tools. We formulate the problem of RNA complex prediction as the determination of the best combination (according to the free energy) of predicted RNA secondary structures and RNA-RNA interactions. Results We model those predicted structures and interactions as a graph in order to have a combinatorial optimization problem that is a constrained maximum weight clique problem. We propose an heuristic based on Breakout Local Search to solve this problem and a tool, called RCPred, that returns several solutions, including motifs like internal and external pseudoknots. On a large number of complexes, RCPred gives competitive results compared to the methods of the state of the art. Conclusions We propose in this paper a method called RCPred for the prediction of several secondary structures of RNA complexes, including internal and external pseudoknots. As further works we will propose an improved computation of the global energy and the insertion of 3D motifs in the RNA complexes.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable. [ABSTRACT FROM AUTHOR]

Maize chlorotic mottle virus (MCMV) combines with a potyvirus in maize lethal necrosis disease (MLND), a serious emerging disease worldwide. To inform resistance strategies, we characterized the translation initiation mechanism of MCMV. We report that MCMV RNA contains a cap-independent translation element (CITE) in its 3' untranslated region (UTR). The MCMV 3' CITE (MTE) was mapped to nucleotides 4164 to 4333 in the genomic RNA. 2'-Hydroxyl acylation analyzed by primer extension (SHAPE) probing revealed that the MTE is a distinct variant of the panicum mosaic virus-like 3' CITE (PTE). Like the PTE, electrophoretic mobility shift assays (EMSAs) indicated that eukaryotic translation initiation factor 4E (eIF4E) binds the MTE despite the absence of an m7GpppN cap structure, which is normally required for eIF4E to bind RNA. Using a luciferase reporter system, mutagenesis to disrupt and restore base pairing revealed that the MTE interacts with the 5' UTRs of both genomic RNA and subgenomic RNA1 via long-distance kissing stem-loop interaction to facilitate translation. The MTE stimulates a relatively low level of translation and has a weak, if any, pseudoknot, which is present in the most active PTEs, mainly because the MTE lacks the pyrimidine-rich tract that base pairs to a G-rich bulge to form the pseudoknot. However, most mutations designed to form a pseudoknot decreased translation activity. Mutations in the viral genome that reduced or restored translation prevented and restored virus replication, respectively, in maize protoplasts and in plants. In summary, the MTE differs from the canonical PTE but falls into a structurally related class of 3' CITEs. [ABSTRACT FROM AUTHOR]

The hepatitis C virus (HCV) genomic RNA contains an internal ribosome entry site (IRES) in its 5' untranslated region, the structure of which is essential for viral protein translation. The IRES includes a predicted pseudoknot interaction near the AUG start codon, but the results of previous studies of its structure have been conflicting. Using mutational analysis coupled with activity and functional assays, we verified the importance of pseudoknot base pairings for IRES-mediated translation and, using 35 mutants, conducted a comprehensive study of the structural tolerance and functional contributions of the pseudoknot. Ribosomal toeprinting experiments show that the entirety of the pseudoknot element positions the initiation codon in the mRNA binding cleft of the 40S ribosomal subunit. Optimal spacing between the pseudoknot and the start site AUG resembles that between the Shine-Dalgarno sequence and the initiation codon in bacterial mRNAs. Finally, we validated the HCV IRES pseudoknot as a potential drug target using antisense 2'-OMe oligonucleotides.

Abstract Background RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. Results We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F1-score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F1-scores are always higher than 70% for any number of solutions returned. Conclusion The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

Comparison of RNA structures is one of the most crucial analysis for elucidating their individual functions and promoting medical applications. Because it is widely accepted that their functions and structures are strongly correlated, various methods for RNA secondary structure analysis have been proposed owing to the difficulty in predicting RNA three-dimensional structure directly from its sequence. However, there are few methods dealing with RNA secondary structures with a specific and complex partial structure called pseudoknot despite its significance to biological process, which is a big obstacle for analyzing their functions. In this study, we propose a novel tree representation of pseudoknotted RNA secondary structures by topological centroid identification and their comparison methods based on the tree edit distance. In the proposed method, a given graph representing an RNA secondary structure is transformed to a tree rooted at one of the vertices constituting the topological centroid that is identified by removing cycles with peeling processing for the graph. When comparing tree-represented RNA secondary structures collected from a public database using the tree edit distance and functional gene groups defined by Gene Ontology (GO), the proposed method showed better clustering results according to their GOs than canonical RNA sequence-based comparison. In addition, we also report a case that the combination of the tree edit distance and the sequence edit distance shows a better classification of the pseudoknotted RNA secondary structures. [ABSTRACT FROM AUTHOR]

Background: RNA pseudoknot structures play an important role in biological processes. However, existing RNA secondary structure prediction algorithms cannot predict the pseudoknot structure efficiently. Although random matching can improve the number of base pairs, these non-consecutive base pairs cannot make contributions to reduce the free energy. Result: In order to improve the efficiency of searching procedure, our algorithm take consecutive base pairs as the basic components. Firstly, our algorithm calculates and archive all the consecutive base pairs in triplet data structure, if the number of consecutive base pairs is greater than given minimum stem length. Secondly, the annealing schedule is adapted to select the optimal solution that has minimum free energy. Finally, the proposed algorithm is evaluated with the real instances in PseudoBase. Conclusion: The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art RNA structure prediction algorithms. [ABSTRACT FROM AUTHOR]

Telomerase is essential formaintaining telomere integrity. Although telomerase function is widely conserved, the integral telomerase RNA (TR) that provides a template for telomeric DNA synthesis has diverged dramatically. Nevertheless, TR molecules retain 2 highly conserved structural domains critical for catalysis: a templateproximal pseudoknot (PK) structure and a downstream stem-loop structure. Here we introduce the authentic TR from the plant Arabidopsis thaliana, called AtTR, identified through next-generation sequencing of RNAs copurifying with Arabidopsis TERT. This RNA is distinct from the RNA previously described as the templating telomerase RNA, AtTER1. AtTR is a 268-nt Pol III transcript necessary for telomere maintenance in vivo and sufficient with TERT to reconstitute telomerase activity in vitro. Bioinformatics analysis identified 85 AtTR orthologs from 3 major clades of plants: angiosperms, gymnosperms, and lycophytes. Through phylogenetic comparisons, a secondary structure model conserved among plant TRs was inferred and verified using in vitro and in vivo chemical probing. The conserved plant TR structure contains a template-PK core domain enclosed by a P1 stem and a 3′ long-stem P4/5/6, both of which resemble a corresponding structural element in ciliate and vertebrate TRs. However, the plant TR contains additional stems and linkers within the template-PK core, allowing for expansion of PK structure from the simple PK in the smaller ciliate TR during evolution. Thus, the plant TR provides an evolutionary bridge that unites the disparate structures of previously characterized TRs from ciliates and vertebrates. [ABSTRACT FROM AUTHOR]

Porcine astrovirus type 3 (PoAstV3) has been previously identified as a cause of polioencephalomyelitis in swine and continues to cause disease in the US swine industry. Herein, we describe the characterization of both untranslated regions, frameshifting signal, putative genome-linked virus protein (VPg) and conserved antigenic epitopes of several novel PoAstV3 genomes. Twenty complete coding sequences (CDS) were obtained from 32 diagnostic cases originating from 11 individual farms/systems sharing a nucleotide (amino acid) percent identity of 89.74–100% (94.79–100%), 91.9–100% (96.3–100%) and 90.71–100% (93.51–100%) for ORF1a, ORF1ab and ORF2, respectively. Our results indicate that the 5′UTR of PoAstV3 is highly conserved highlighting the importance of this region in translation initiation while their 3′UTR is moderately conserved among strains, presenting alternative configurations including multiple putative protein binding sites and pseudoknots. Moreover, two predicted conserved antigenic epitopes were identified matching the 3′ termini of VP27 of PoAstV3 USA strains. These epitopes may aid in the design and development of vaccine components and diagnostic assays useful to control outbreaks of PoAstV3-associated CNS disease. In conclusion, this is the first analysis predicting the structure of important regulatory motifs of neurotropic mamastroviruses, which differ from those previously described in human astroviruses.

In this review paper we discuss fatgraphs as a conceptual framework for RNA structures. We discuss various notions of coarse-grained RNA structures and relate them to fatgraphs.We motivate and discuss the main intuition behind the fatgraph model and showcase its applicability to canonical as well as noncanonical base pairs. Recent discoveries regarding novel recursions of pseudoknotted (pk) configurations as well as their translation into context-free grammars for pk-structures are discussed. This is shown to allow for extending the concept of partition functions of sequences w.r.t. a fixed structure having non-crossing arcs to pk-structures. We discuss minimum free energy folding of pk-structures and combine these above results outlining how to obtain an inverse folding algorithm for PK structures.

We recently implemented a bioinformatics pipeline that can uncover novel, but rare, riboswitch candidates as well as other noncoding RNA structures in bacteria. A prominent candidate revealed by our initial search efforts was called the ‘thiS motif’ because of its frequent association with a gene coding for the ThiS protein, which delivers sulfur to form the thiazole moiety of the thiamin precursor HET-P. In the current report, we describe biochemical and genetic data demonstrating that thiS motif RNAs function as sensors of the thiamin precursor HMP-PP, which is fused with HET-P ultimately to form the final active coenzyme thiamin pyrophosphate (TPP). HMP-PP riboswitches exhibit a distinctive architecture wherein an unusually small ligand-sensing aptamer is almost entirely embedded within an otherwise classic intrinsic transcription terminator stem. This arrangement yields remarkably compact genetic switches that bacteria use to tune the levels of thiamin precursors during the biosynthesis of this universally distributed coenzyme.

While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, “DMfold,” a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database.

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In this paper we analyze the length-spectrum of blocks in γ -structures. γ -structures are a class of RNA pseudoknot structures that play a key role in the context of polynomial time RNA folding. A γ -structure is constructed by nesting and concatenating specific building components having topological genus at most γ . A block is a substructure enclosed by crossing maximal arcs with respect to the partial order induced by nesting. We show that, in uniformly generated γ -structures, there is a significant gap in this length-spectrum, i.e., there asymptotically almost surely exists a unique longest block of length at least n - O (n 1 / 2) and that with high probability any other block has finite length. For fixed γ , we prove that the length of the complement of the longest block converges to a discrete limit law, and that the distribution of short blocks of given length tends to a negative binomial distribution in the limit of long sequences. We refine this analysis to the length spectrum of blocks of specific pseudoknot types, such as H-type and kissing hairpins. Our results generalize the rainbow spectrum on secondary structures by the first and third authors and are being put into context with the structural prediction of long non-coding RNAs. [ABSTRACT FROM AUTHOR]

Background: RNAs can interact and form complexes, which have various biological roles. The secondary structure prediction of those complexes is a first step towards the identification of their 3D structure. We propose an original approach that takes advantage of the high number of RNA secondary structure and RNA-RNA interaction prediction tools. We formulate the problem of RNA complex prediction as the determination of the best combination (according to the free energy) of predicted RNA secondary structures and RNA-RNA interactions. Results: We model those predicted structures and interactions as a graph in order to have a combinatorial optimization problem that is a constrained maximum weight clique problem. We propose an heuristic based on Breakout Local Search to solve this problem and a tool, called RCPred, that returns several solutions, including motifs like internal and external pseudoknots. On a large number of complexes, RCPred gives competitive results compared to the methods of the state of the art. Conclusions: We propose in this paper a method called RCPred for the prediction of several secondary structures of RNA complexes, including internal and external pseudoknots. As further works we will propose an improved computation of the global energy and the insertion of 3D motifs in the RNA complexes. [ABSTRACT FROM AUTHOR]

While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, "DMfold," a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database. [ABSTRACT FROM AUTHOR]

Recently, a framework considering RNA sequences and their RNA secondary structures as pairs led to some information-theoretic perspectives on how the semantics encoded in RNA sequences can be inferred. This pairing arises naturally from the energy model of RNA secondary structures. Fixing the sequence in the pairing produces the RNA energy landscape, whose partition function was discovered by McCaskill. Dually, fixing the structure induces the energy landscape of sequences. The latter has been considered originally for designing more efficient inverse folding algorithms and subsequently enhanced by facilitating the sampling of sequences. We present here a partition function of sequence/structure pairs, with endowed Hamming distance and base pair distance filtration. This partition function is an augmentation of the previous mentioned (dual) partition function. We develop an efficient dynamic programming routine to recursively compute the partition function with this double filtration. Our framework is capable of dealing with RNA secondary structures as well as 1-structures, where a 1-structure is an RNA pseudoknot structure consisting of "building blocks" of genus 0 or 1. In particular, 0-structures, consisting of only "building blocks" of genus 0, are exactly RNA secondary structures. The time complexity for calculating the partition function of 1-pairs, that is, sequence/structure pairs where the structures are 1-structures, is O(h3b3n6), whereh, b, ndenote the Hamming distance, base pair distance, and sequence length, respectively. The time complexity for the partition function of 0-pairs is O(h2b2n3). [ABSTRACT FROM AUTHOR]

The functions of RNA molecules are defined by their spatial structure, whose folding is regulated by numerous factors making RNA very similar to proteins. Prediction of RNA folding nuclei gives the possibility to take a fresh look at the problems of the multiple folding pathways of RNA molecules and RNA stability. The algorithm previously developed for prediction of protein folding nuclei has been successfully applied to ~150 various RNA structures: hairpins, tRNAs, structures with pseudoknots, and the large structured P4-P6 domain of the Tetrahymena group I intron RNA. The calculated Φ-values for tRNA structures agree with the experimental data obtained earlier. According to the experiment the nucleotides of the D and T hairpin loops are the last to be involved in the tRNA tertiary structure. Such agreement allowed us to do a prediction for an example of large structured RNA, the P4-P6 RNA domain. One of the advantages of our method is that it allows us to make predictions about the folding nucleus for nontrivial RNA motifs: pseudoknots and tRNA.

Riboswitches are small noncoding RNAs found primarily in the 5' leader regions of bacterial messenger RNAs where they regulate expression of downstream genes in response to binding one or more cellular metabolites. Such noncoding RNAs are often regulated at the translation level, which is thought to be mediated by the accessibility of the Shine-Dalgarno sequence (SDS) ribosome-binding site. Three classes (I-III) of prequeuosine 1 (preQ 1 )-sensing riboswitches are known that control translation. Class I is divided into three subtypes (types I-III) that have diverse mechanisms of sensing preQ 1 , which is involved in queuosine biosynthesis. To provide insight into translation control, we determined a 2.30 Å-resolution cocrystal structure of a class I type III preQ 1 -sensing riboswitch identified in Escherichia coli (Eco) by bioinformatic searches. The Eco riboswitch structure differs from previous preQ 1 riboswitch structures because it has the smallest naturally occurring aptamer and the SDS directly contacts the preQ 1 metabolite. We validated structural observations using surface plasmon resonance and in vivo gene-expression assays, which showed strong switching in live E. coli. Our results demonstrate that the Eco riboswitch is relatively sensitive to mutations that disrupt noncanonical interactions that form the pseudoknot. In contrast to type II preQ 1 riboswitches, a kinetic analysis showed that the type III Eco riboswitch strongly prefers preQ 1 over the chemically similar metabolic precursor preQ 0 . Our results reveal the importance of noncanonical interactions in riboswitch-driven gene regulation and the versatility of the class I preQ 1 riboswitch pseudoknot as a metabolite-sensing platform that supports SDS sequestration., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Protein synthesis by the ribosome is the final stage of biological information transfer and represents an irreversible commitment to gene expression. Accurate translation of messenger RNA is therefore essential to all life, and spontaneous errors by the translational machinery are highly infrequent (∼1/100,000 codons). Programmed -1 ribosomal frameshifting (-1PRF) is a mechanism in which the elongating ribosome is induced at high frequency to slip backward by one nucleotide at a defined position and to continue translation in the new reading frame. This is exploited as a translational regulation strategy by hundreds of RNA viruses, which rely on -1PRF during genome translation to control the stoichiometry of viral proteins. While early investigations of -1PRF focused on virological and biochemical aspects, the application of X-ray crystallography and cryo-electron microscopy (cryo-EM), and the advent of deep sequencing and single-molecule approaches have revealed unexpected structural diversity and mechanistic complexity. Molecular players from several model systems have now been characterized in detail, both in isolation and, more recently, in the context of the elongating ribosome. Here we provide a summary of recent advances and discuss to what extent a general model for -1PRF remains a useful way of thinking.