Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

Carrimycin inhibits coronavirus replication by decreasing the efficiency of programmed -1 ribosomal frameshifting through directly binding to the RNA pseudoknot of viral frameshift-stimulatory element.

The pandemic of SARS-CoV-2 worldwide with successive emerging variants urgently calls for small-molecule oral drugs with broad-spectrum antiviral activity. Here, we show that carrimycin, a new macrolide antibiotic in the clinic and an antiviral candidate for SARS-CoV-2 in phase III trials, decreases the efficiency of programmed -1 ribosomal frameshifting of coronaviruses and thus impedes viral replication in a broad-spectrum fashion. Carrimycin binds directly to the coronaviral frameshift-stimulatory element (FSE) RNA pseudoknot, interrupting the viral protein translation switch from ORF1a to ORF1b and thereby reducing the level of the core components of the viral replication and transcription complexes. Combined carrimycin with known viral replicase inhibitors yielded a synergistic inhibitory effect on coronaviruses. Because the FSE mechanism is essential in all coronaviruses, carrimycin could be a new broad-spectrum antiviral drug for human coronaviruses by directly targeting the conserved coronaviral FSE RNA. This finding may open a new direction in antiviral drug discovery for coronavirus variants.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2.

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

Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes.

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.

TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots.

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.

Structure and function analysis of a type III preQ1-I riboswitch from Escherichia coli reveals direct metabolite sensing by the Shine-Dalgarno sequence.

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 prequeuosine1 (preQ1)-sensing riboswitches are known that control translation. Class I is divided into three subtypes (types I-III) that have diverse mechanisms of sensing preQ1, 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 preQ1-sensing riboswitch identified in Escherichia coli (Eco) by bioinformatic searches. The Eco riboswitch structure differs from previous preQ1 riboswitch structures because it has the smallest naturally occurring aptamer and the SDS directly contacts the preQ1 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 preQ1 riboswitches, a kinetic analysis showed that the type III Eco riboswitch strongly prefers preQ1 over the chemically similar metabolic precursor preQ0. Our results reveal the importance of noncanonical interactions in riboswitch-driven gene regulation and the versatility of the class I preQ1 riboswitch pseudoknot as a metabolite-sensing platform that supports SDS sequestration.

Investigating the correlation between Xrn1-resistant RNAs and frameshifter pseudoknots.

Xrn1-resistant RNA structures are multifunctional elements employed by an increasing number of RNA viruses. One of such elements is the coremin motif, discovered in plant virus RNAs, of which the structure has been hypothesized to form a yet unelucidated pseudoknot. Recently, the coremin motif was shown to be capable of stalling not only Xrn1, but scanning ribosomes as well. Following that observation, in this study we demonstrate that the coremin motif can promote -1 ribosomal frameshifting, similar to better-characterized viral frameshifting pseudoknots. Since this function was lost in concert with substitutions that were known to disturb Xrn1-resistance, we developed a frameshifting screen for finding novel Xrn1-resistant RNAs by randomizing parts of the coremin motif. This yielded new insights into the coremin motif structure, as Xrn1-resistant variations were identified that more clearly indicate a pseudoknot interaction. In addition, we show that the Xrn1-resistant RNA of Zika virus promotes frameshifting as well, while known -1 programmed ribosomal frameshifting pseudoknots do not stall Xrn1, suggesting that promoting frameshifting is a universal characteristic of Xrn1-resistant RNAs, but that Xrn1-resistance requires more than just a frameshifting pseudoknot.

Right Coronary Artery Button Pseudoaneurysm After the Modified Bentall Procedure.

Anastomoses of the coronary buttons are the Achilles' heel of the modified Bentall procedure (MBP) for the repair of the aortic root and ascending aorta. We present a rare case of post-MBP right coronary artery button pseudoaneurysm in a 30-year-old man. The contained leak, attributed to a pseudoknot in the polypropylene suture, was visualized via computed tomography angiography and transesophageal echocardiogram and repaired under deep hypothermic circulatory arrest.

Structure of LARP7 Protein p65-telomerase RNA Complex in Telomerase Revealed by Cryo-EM and NMR.

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.

Structural and Functional Insights into Viral Programmed Ribosomal Frameshifting.

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.

Evolution of coronavirus frameshifting elements: Competing stem networks explain conservation and variability.

The frameshifting RNA element (FSE) in coronaviruses (CoVs) regulates the programmed -1 ribosomal frameshift (-1 PRF) mechanism common to many viruses. The FSE is of particular interest as a promising drug candidate. Its associated pseudoknot or stem loop structure is thought to play a large role in frameshifting and thus viral protein production. To investigate the FSE structural evolution, we use our graph theory-based methods for representing RNA secondary structures in the RNA-As-Graphs (RAG) framework to calculate conformational landscapes of viral FSEs with increasing sequence lengths for representative 10 Alpha and 13 Beta-CoVs. By following length-dependent conformational changes, we show that FSE sequences encode many possible competing stems which in turn favor certain FSE topologies, including a variety of pseudoknots, stem loops, and junctions. We explain alternative competing stems and topological FSE changes by recurring patterns of mutations. At the same time, FSE topology robustness can be understood by shifted stems within different sequence contexts and base pair coevolution. We further propose that the topology changes reflected by length-dependent conformations contribute to tuning the frameshifting efficiency. Our work provides tools to analyze virus sequence/structure correlations, explains how sequence and FSE structure have evolved for CoVs, and provides insights into potential mutations for therapeutic applications against a broad spectrum of CoV FSEs by targeting key sequence/structural transitions.

Improved prime editing allows for routine predictable gene editing in Physcomitrium patens.

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 3' 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 Ppdek10 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.

A non-invasive wearable stress patch for real-time cortisol monitoring using a pseudoknot-assisted aptamer.

Stress is part of everyone's life and is exacerbated by traumatic events such as pandemics, disasters, violence, lifestyle changes, and health disorders. Chronic stress has many detrimental health effects and can even be life-threatening. Long-term stress monitoring outside of a hospital is often accomplished by measuring heart rate variability. While easy to measure, this digital biomarker has low specificity, greatly limiting its utility. To address this shortcoming, we report a non-invasive, wearable biomolecular sensor to monitor cortisol levels in sweat. Cortisol is a neuroendocrine hormone that regulates homeostasis as part of the stress pathway. Cortisol is detected using an electrochemical sensor functionalized with a pseudoknot-assisted aptamer and a flexible microfluidic sweat sampling system. The skin-worn microfluidic sampler provides rapid sweat collection while separating old and new sweat. The conformation-switching aptamer provides high specificity towards cortisol while being regenerable, allowing it to monitor temporal changes continuously. The aptamer was engineered to add a pseudoknot, restricting it to only two states, thus minimizing the background signal and enabling high sensitivity. An electrochemical pH sensor allows pH-corrected amperometric measurements. Device operation was demonstrated invitro with a broad linear dynamic range (1 pM - 1 µM) covering the physiological range and a sub-picomolar (0.2 pM) limit of detection in sweat. Real-time, on-body measurements were collected from human subjects using an induced stress protocol, demonstrating in-situ signal regeneration and the ability to detect dynamic cortisol fluctuations continuously for up to 90 min. The reported device has the potential to improve prognosis and enable personalized treatments.

A potential long-range RNA-RNA interaction in the HIV-1 RNA.

It is well-established that viral and cellular mRNAs alike harbour functional long-range intra-molecular RNA-RNA interactions. Despite the biological importance of such interactions, their identification and characterization remain challenging. Here we present a computational method for the identification of certain kinds of long-range intra-molecular RNA-RNA interactions involving the loop nucleotides of a hairpin loop. Using the computational method, we analysed 4272 HIV-1 genomic mRNAs. A potential long-range intra-molecular RNA-RNA interaction within the HIV-1 genomic RNA was identified. The long-range interaction is mediated by a kissing loop structure between two stem-loops of the previously reported SHAPE-based secondary structure of the entire HIV-1 genome. Structural modelling studies were carried out to show that the kissing loop structure not only is sterically feasible, but also contains a conserved RNA structural motif often found in compact RNA pseudoknots. The computational method should be generally applicable to the identification of potential long-range intra-molecular RNA-RNA interactions in any viral or cellular mRNA sequence.Communicated by Ramaswamy H. Sarma.

Pseudoknot-targeting Cas13b combats SARS-CoV-2 infection by suppressing viral replication.

CRISPR-Cas13-mediated viral genome targeting is a novel strategy for defending against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Here, we generated mRNA-encoded Cas13b targeting the open reading frame 1b (ORF1b) region to effectively degrade the RNA-dependent RNA polymerase gene. Of the 12 designed CRISPR RNAs (crRNAs), those targeting the pseudoknot site upstream of ORF1b were found to be the most effective in suppressing SARS-CoV-2 propagation. Pseudoknot-targeting Cas13b reduced expression of the spike protein and attenuated viral replication by 99%. It also inhibited the replication of multiple SARS-CoV-2 variants, exhibiting broad potency. We validated the therapeutic efficacy of this system in SARS-CoV-2-infected hACE2 transgenic mice, demonstrating that crRNA treatment significantly reduced viral titers. Our findings suggest that the pseudoknot region is a strategic site for targeted genomic degradation of SARS-CoV-2. Hence, pseudoknot-targeting Cas13b could be a breakthrough therapy for overcoming infections by SARS-CoV-2 or other RNA viruses.

Computational design and experimental verification of pseudoknotted ribozymes.

The design of new RNA sequences that retain the function of a model RNA structure is a challenge in bioinformatics because of the structural complexity of these molecules. RNA can fold into its secondary and tertiary structures by forming stem-loops and pseudoknots. A pseudoknot is a set of base pairs between a region within a stem-loop and nucleotides outside of this stem-loop; this motif is very important for numerous functional structures. It is important for any computational design algorithm to take into account these interactions to give a reliable result for any structures that include pseudoknots. In our study, we experimentally validated synthetic ribozymes designed by Enzymer, which implements algorithms allowing for the design of pseudoknots. Enzymer is a program that uses an inverse folding approach to design pseudoknotted RNAs; we used it in this study to design two types of ribozymes. The ribozymes tested were the hammerhead and the glmS, which have a self-cleaving activity that allows them to liberate the new RNA genome copy during rolling-circle replication or to control the expression of the downstream genes, respectively. We demonstrated the efficiency of Enzymer by showing that the pseudoknotted hammerhead and glmS ribozymes sequences it designed were extensively modified compared to wild-type sequences and were still active.

REDfold: accurate RNA secondary structure prediction using residual encoder-decoder network.

BACKGROUND: As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA secondary prediction is mainly based on the thermodynamic model with dynamic programming to find the optimal structure. However, the prediction performance based on the traditional approach is unsatisfactory for further research. Besides, the computational complexity of the structure prediction using dynamic programming is [Formula: see text]; it becomes [Formula: see text] for RNA structure with pseudoknots, which is computationally impractical for large-scale analysis. RESULTS: In this paper, we propose REDfold, a novel deep learning-based method for RNA secondary prediction. REDfold utilizes an encoder-decoder network based on CNN to learn the short and long range dependencies among the RNA sequence, and the network is further integrated with symmetric skip connections to efficiently propagate activation information across layers. Moreover, the network output is post-processed with constrained optimization to yield favorable predictions even for RNAs with pseudoknots. Experimental results based on the ncRNA database demonstrate that REDfold achieves better performance in terms of efficiency and accuracy, outperforming the contemporary state-of-the-art methods.

Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops.

The accurate "base pairing" in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar's advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.

HIV co-opts a cellular antiviral mechanism, activation of stress kinase PKR by its RNA, to enable splicing of rev/tat mRNA.

BACKGROUND: Activation of RNA-dependent stress kinase PKR, especially by viral double-stranded RNA, induces eukaryotic initiation factor 2 α-chain (eIF2α) phosphorylation, attenuating thereby translation. We report that this RNA-mediated negative control mechanism, considered a cornerstone of the cell's antiviral response, positively regulates splicing of a viral mRNA. RESULTS: Excision of the large human immunodeficiency virus (HIV) rev/tat intron depends strictly on activation of PKR by the viral RNA and on eIF2α phosphorylation. Rev/tat mRNA splicing was blocked by viral PKR antagonists Vaccinia E3L and Ebola VP35, as well as by a trans-dominant negative mutant of PKR, yet enhanced by overexpressing PKR. Expression of non-phosphorylatable mutant eIF2αS51A, but not of wild type eIF2α, abrogated efficient splicing of rev/tat mRNA. By contrast, expression of eIF2αS51D, a phosphomimetic mutant of eIF2α, left rev/tat mRNA splicing intact. Unlike eIF2αS51A, eIF2αS51D does not inhibit eIF2α phosphorylation by activated PKR. All HIV mRNA species contain terminal trans-activation response (TAR) stem-loop sequences that potentially could activate PKR, yet even upon TAR deletion, HIV mRNA production remained sensitive to inhibitors of PKR activation. Bioinformatic and mutational analyses revealed a compact RNA pseudoknot upstream of 3'-terminal TAR that promotes splicing by activating PKR. Supporting its essential role in control of splicing, this pseudoknot is conserved among diverse HIV and nonhuman primate SIVcpz isolates. The pseudoknot and 3'-terminal TAR collaborate in mediating PKR-regulated splicing of rev/tat intron, the pseudoknot being dominant. CONCLUSIONS: Our results on HIV provide the first example of a virus co-opting activation of PKR by its RNA, a cellular antiviral mechanism, to promote splicing. They raise the question whether other viruses may use local activation of host kinase PKR through RNA elements within their genome to achieve efficient splicing of their mRNA. Our experiments reveal an indispensable role for eIF2α phosphorylation in HIV rev/tat mRNA splicing that accounts for the need for PKR activation.

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.