Pseudoknots: RNA structures with diverse functions

An enhanced Eco1 retron editor enables precision genome engineering in human cells from a single-copy integrated lentivirus

Retrons are a retroelement class found in diverse prokaryotes that can be adapted to augment CRISPR-Cas9 genome engineering technology to efficiently rewrite short stretches of genetic information in bacteria and yeast; however, efficiency in human cells has been limited by unknown factors. We identified non-coding RNA (ncRNA) instability and impaired Cas9 activity as major contributors to poor retron editor efficiency. We re-engineered the Eco1 ncRNA to incorporate an exoribonuclease-resistant RNA pseudoknot from the Zika virus 3' UTR and devised an RNA processing strategy using Csy4 ribonuclease to liberate the sgRNA and ncRNA. These modifications yielded a ncRNA with 5'- and 3'-end protection and an sgRNA with minimal 5' extension. This strategy increased steady-state ncRNA levels and rescued Cas9 activity leading to enhanced efficiency of the Eco1 retron editor in human cells. The enhanced Eco1 retron editor enabled the insertion of missense mutations in human cells from a single integrated lentivirus, thereby ensuring genotype-phenotype linkage over multiple cell divisions. This work reveals a previously unappreciated role for ncRNA stability in retron editor efficiency in human cells. Here we present an enhanced Eco1 retron editor that enables efficient introduction of missense mutations in human cells from a single heritable genome copy.

Assessing RNA atomistic force fields via energy landscape explorations in implicit solvent

Predicting the structure and dynamics of RNA molecules still proves challenging because of the relative scarcity of experimental RNA structures on which to train models and the very sensitive nature of RNA towards its environment. In the last decade, several atomistic force fields specifically designed for RNA have been proposed and are commonly used for simulations. However, it is not necessarily clear which force field is the most suitable for a given RNA molecule. In this contribution, we propose the use of the computational energy landscape framework to explore the energy landscape of RNA systems as it can bring complementary information to the more standard approaches of enhanced sampling simulations based on molecular dynamics. We apply the EL framework to the study of a small RNA pseudoknot, the Aquifex aeolicus tmRNA pseudoknot PK1, and we compare the results of five different RNA force fields currently available in the AMBER simulation software, in implicit solvent. With this computational approach, we can not only compare the predicted 'native' states for the different force fields, but the method enables us to study metastable states as well. As a result, our comparison not only looks at structural features of low energy folded structures, but provides insight into folding pathways and higher energy excited states, opening to the possibility of assessing the validity of force fields also based on kinetics and experiments providing information on metastable and unfolded states.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-024-01202-9.

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.

DinoKnot: Duplex Interaction of Nucleic Acids With PseudoKnots

Interaction of nucleic acid molecules is essential for their functional roles in the cell and their applications in biotechnology. While simple duplex interactions have been studied before, the problem of efficiently predicting the minimum free energy structure of more complex interactions with possibly pseudoknotted structures remains a challenge. In this work, we introduce a novel and efficient algorithm for prediction of Duplex Interaction of Nucleic acids with pseudoKnots, DinoKnot follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (both homo- and hetero-dimers). DinoKnot utilizes the structure of molecules before interaction as a guide to find their duplex structure allowing for possible base pair competitions. To showcase DinoKnots's capabilities we evaluated its predicted structures against (1) experimental results for SARS-CoV-2 genome and nine primer-probe sets, (2) a clinically verified example of a mutation affecting detection, and (3) a known nucleic acid interaction involving a pseudoknot. In addition, we compared our results against our closest competition, RNAcofold, further highlighting DinoKnot's strengths. We believe DinoKnot can be utilized for various applications including screening new variants for potential detection issues and supporting existing applications involving DNA/RNA interactions, adding structural considerations to the interaction to elicit functional information.

RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag

RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.

Excited States of apo-Guanidine-III Riboswitch Contribute to Guanidinium Binding through Both Conformational and Induced-Fit Mechanisms

Riboswitches are mRNA segments that regulate gene expression through conformational changes driven by their cognate ligand binding. The ykkC motif forms a riboswitch class that selectively senses a guanidinium ion (Gdm+) and regulates the downstream expression of proteins which aid in the efflux of excess Gdm+ from the cells. The aptamer domain (AD) of the guanidine-III riboswitch forms an H-type pseudoknot with a triple helical domain that binds a Gdm+. We studied the binding of Gdm+ to the AD of the guanidine (ykkC)-III riboswitch using computer simulations to probe the specificity of the riboswitch to Gdm+ binding. We show that Gdm+ binding is a fast process occurring on the nanosecond time scale, with minimal conformational changes to the AD. Using machine learning and Markov-state models, we identified the excited conformational states of the AD, which have a high Gdm+ binding propensity, making the Gdm+ binding landscape complex exhibiting both conformational selection and induced-fit mechanisms. The proposed apo-AD excited states and their role in the ligand-sensing mechanism are amenable to experimental verification. Further, targeting these excited-state conformations in discovering new antibiotics can be explored.

Diversity of Self-Assembled RNA Complexes: From Nanoarchitecture to Nanomachines

New tool development for various nucleic acid applications is an essential task in RNA nanotechnology. Here, we determined the ability of self-limited complex formation by a pair of oligoribonucleotides carrying two pairwise complementary blocks connected by a linker of different lengths in each chain. The complexes were analyzed using UV melting, gel shift assay analysis, and molecular dynamics (MD) simulations. We have demonstrated the spontaneous formation of various self-limited and concatemer complexes. The linear concatemer complex is formed by a pair of oligomers without a linker in at least one of them. Longer linkers resulted in the formation of circular complexes. The self-limited complexes formation was confirmed using the toehold strand displacement. The MD simulations indicate the reliability of the complexes' structure and demonstrate their dynamics, which increase with the rise of complex size. The linearization of 2D circular complexes into 1D structures and a reverse cyclization process were demonstrated using a toehold-mediated approach. The approach proposed here for the construction and directed modification of the molecularity and shape of complexes will be a valuable tool in RNA nanotechnology, especially for the rational design of therapeutic nucleic acids with high target specificity and the programmable response of the immune system of organisms.

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

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

Atomistic structure of the SARS-CoV-2 pseudoknot in solution from SAXS-driven molecular dynamics

SARS-CoV-2 depends on -1 programmed ribosomal frameshifting (-1 PRF) to express proteins essential for its replication. The RNA pseudoknot stimulating -1 PRF is thus an attractive drug target. However, the structural models of this pseudoknot obtained from cryo-EM and crystallography differ in some important features, leaving the pseudoknot structure unclear. We measured the solution structure of the pseudoknot using small-angle X-ray scattering (SAXS). The measured profile did not agree with profiles computed from the previously solved structures. Beginning with each of these solved structures, we used the SAXS data to direct all atom molecular dynamics (MD) simulations to improve the agreement in profiles. In all cases, this refinement resulted in a bent conformation that more closely resembled the cryo-EM structures than the crystal structure. Applying the same approach to a point mutant abolishing -1 PRF revealed a notably more bent structure with reoriented helices. This work clarifies the dynamic structures of the SARS-CoV-2 pseudoknot in solution.

Linking folding dynamics and function of SAM/SAH riboswitches at the single molecule level

Riboswitches are regulatory elements found in bacterial mRNAs that control downstream gene expression through ligand-induced conformational changes. Here, we used single-molecule FRET to map the conformational landscape of the translational SAM/SAH riboswitch and probe how co-transcriptional ligand-induced conformational changes affect its translation regulation function. Riboswitch folding is highly heterogeneous, suggesting a rugged conformational landscape that allows for sampling of the ligand-bound conformation even in the absence of ligand. The addition of ligand shifts the landscape, favoring the ligand-bound conformation. Mutation studies identified a key structural element, the pseudoknot helix, that is crucial for determining ligand-free conformations and their ligand responsiveness. We also investigated ribosomal binding site accessibility under two scenarios: pre-folding and co-transcriptional folding. The regulatory function of the SAM/SAH riboswitch involves kinetically favoring ligand binding, but co-transcriptional folding reduces this preference with a less compact initial conformation that exposes the Shine-Dalgarno sequence and takes min to redistribute to more compact conformations of the pre-folded riboswitch. Such slow equilibration decreases the effective ligand affinity. Overall, our study provides a deeper understanding of the complex folding process and how the riboswitch adapts its folding pattern in response to ligand, modulates ribosome accessibility and the role of co-transcriptional folding in these processes.

Crystal structure of a cap-independent translation enhancer RNA

In eukaryotic messenger RNAs, the 5' cap structure binds to the translation initiation factor 4E to facilitate early stages of translation. Although many plant viruses lack the 5' cap structure, some contain cap-independent translation elements (CITEs) in their 3' untranslated region. The PTE (Panicum mosaic virus translation element) class of CITEs contains a G-rich asymmetric bulge and a C-rich helical junction that were proposed to interact via formation of a pseudoknot. SHAPE analysis of PTE homologs reveals a highly reactive guanosine residue within the G-rich region proposed to mediate eukaryotic initiation factor 4E (eIF4E) recognition. Here we have obtained the crystal structure of the PTE from Pea enation mosaic virus 2 (PEMV2) RNA in complex with our structural chaperone, Fab BL3-6. The structure reveals that the G-rich and C-rich regions interact through a complex network of interactions distinct from those expected for a pseudoknot. The motif, which contains a short parallel duplex, provides a structural mechanism for how the guanosine is extruded from the core stack to enable eIF4E recognition. Homologous PTE elements harbor a G-rich bulge and a three-way junction and exhibit covariation at crucial positions, suggesting that the PEMV2 tertiary architecture is conserved among these homologs.

Direct observation of tRNA-chaperoned folding of a dynamic mRNA ensemble

T-box riboswitches are multi-domain noncoding RNAs that surveil individual amino acid availabilities in most Gram-positive bacteria. T-boxes directly bind specific tRNAs, query their aminoacylation status to detect starvation, and feedback control the transcription or translation of downstream amino-acid metabolic genes. Most T-boxes rapidly recruit their cognate tRNA ligands through an intricate three-way stem I-stem II-tRNA interaction, whose establishment is not understood. Using single-molecule FRET, SAXS, and time-resolved fluorescence, we find that the free T-box RNA assumes a broad distribution of open, semi-open, and closed conformations that only slowly interconvert. tRNA directly binds all three conformers with distinct kinetics, triggers nearly instantaneous collapses of the open conformations, and returns the T-box RNA to their pre-binding conformations upon dissociation. This scissors-like dynamic behavior is enabled by a hinge-like pseudoknot domain which poises the T-box for rapid tRNA-induced domain closure. This study reveals tRNA-chaperoned folding of flexible, multi-domain mRNAs through a Venus flytrap-like mechanism.

Hierarchical Hydration Dynamics of RNA with Nano-Water-Pool at Its Core

Many functional RNAs fold into a compact, roughly globular shape by minimizing the electrostatic repulsion between their negatively charged phosphodiester backbone. The fold of such close, compact RNA architecture is often so designed that its outer surface and complex core both are predominately populated by phosphate groups loosely sequestering bases in the intermediate layers. A number of helical junctions maintain the RNA core and its nano-water-pool. While the folding of RNA is manifested by its counterion environment composed of mixed mono- and divalent salts, the concerted role of ion and water in maintaining an RNA fold is yet to be explored. In this work, detailed atomistic simulations of SAM-I and Add Adenine riboswitch aptamers, and subgenomic flavivirus RNA (sfRNA) have been performed in a physiological mixed mono- and divalent salt environment. All three RNA systems have compact folds with a core diameter of range 1-1.7 nm. The spatiotemporal heterogeneity of RNA hydration was probed in a layer-wise manner by distinguishing the core, the intermediate, and the outer layers. The layer-wise decomposition of hydrogen bonds and collective single-particle reorientational dynamics reveal a nonmonotonic relaxation pattern with the slowest relaxation observed at the intermediate layers that involves functionally important tertiary motifs. The slowness of this intermediate layer is attributed to two types of long-resident water molecules: (i) water from ion-hydration layers and (ii) structurally trapped water (distant from ions). The relaxation kinetics of the core and the surface water essentially exposed to the phosphate groups show well-separated time scales from the intermediate layers. In the slow intermediate layers, site-specific ions and water control the functional dynamics of important RNA motifs like kink-turn, observed in different structure-probing experiments. Most interestingly, we find that as the size of the RNA core increases (SAM1 core < sfRNAcore < Add adenine core), its hydration tends to show faster relaxation. The hierarchical hydration and the layer-wise base-phosphate composition uniquely portray the globular RNA to act like a soft vesicle with a quasi-dynamic nano-water-pool at its core.

Machine learning modeling of RNA structures: methods, challenges and future perspectives

The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.

Systematic detection of tertiary structural modules in large RNAs and RNP interfaces by Tb-seq

Compact RNA structural motifs control many aspects of gene expression, but we lack methods for finding these structures in the vast expanse of multi-kilobase RNAs. To adopt specific 3-D shapes, many RNA modules must compress their RNA backbones together, bringing negatively charged phosphates into close proximity. This is often accomplished by recruiting multivalent cations (usually Mg2+), which stabilize these sites and neutralize regions of local negative charge. Coordinated lanthanide ions, such as terbium (III) (Tb3+), can also be recruited to these sites, where they induce efficient RNA cleavage, thereby revealing compact RNA 3-D modules. Until now, Tb3+ cleavage sites were monitored via low-throughput biochemical methods only applicable to small RNAs. Here we present Tb-seq, a high-throughput sequencing method for detecting compact tertiary structures in large RNAs. Tb-seq detects sharp backbone turns found in RNA tertiary structures and RNP interfaces, providing a way to scan transcriptomes for stable structural modules and potential riboregulatory motifs.

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.

Molecular Apomorphies in the Secondary and Tertiary Structures of Length-Variable Regions (LVRs) of 18S rRNA Shed Light on the Systematic Position of the Family Thaumastellidae (Hemiptera: Heteroptera: Pentatomoidea)

The SSU nrDNA, a small subunit of the nuclear ribosomal DNA (coding 18S rRNA), is one of the most frequently sequenced genes in molecular studies in Hexapoda. In insects, including true bugs (Hemiptera: Heteroptera), only its primary structures (i.e., aligned sequences) are predominantly used in phylogenetic reconstructions. It is known that including RNA secondary structures in the alignment procedure is essential for improving accuracy and robustness in phylogenetic tree reconstruction. Moreover, local plasticity in rRNAs might impact their tertiary structures and corresponding functions. To determine the systematic position of Thaumastellidae within the superfamily Pentatomoidea, the secondary and-for the first time among all Hexapoda-tertiary structures of 18S rRNAs in twelve pentatomoid families were compared and analysed. Results indicate that the shapes of the secondary and tertiary structures of the length-variable regions (LVRs) in the 18S rRNA are phylogenetically highly informative. Based on these results, it is suggested that the Thaumastellidae is maintained as an independent family within the superfamily Pentatomoidea, rather than as a part of the family Cydnidae. Moreover, the analyses indicate a close relationship between Sehirinae and Parastrachiidae, expressed in morpho-molecular synapomorphies in the predicted secondary and tertiary structures of the length-variable region L (LVR L).

The Functional RNA Identification (FRID) Pipeline: Identification of Potential Pseudoknot-Containing RNA Elements as Therapeutic Targets for SARS-CoV-2

The COVID-19 pandemic persists despite the development of effective vaccines. As such, it remains crucial to identify new targets for antiviral therapies. The causative virus of COVID-19, SARS-CoV-2, is a positive-sense RNA virus with RNA structures that could serve as therapeutic targets. One such RNA with established function is the frameshift stimulatory element (FSE), which promotes programmed ribosomal frameshifting. To accelerate identification of additional functional RNA elements, we introduce a novel computational approach termed the Functional RNA Identification (FRID) pipeline. The guiding principle of our pipeline, which uses established component programs as well as customized component programs, is that functional RNA elements have conserved secondary and pseudoknot structures that facilitate function. To assess the presence and conservation of putative functional RNA elements in SARS-CoV-2, we compared over 6,000 SARS-CoV-2 genomic isolates. We identified 22 functional RNA elements from the SARS-CoV-2 genome, 14 of which have conserved pseudoknots and serve as potential targets for small molecule or antisense oligonucleotide therapeutics. The FRID pipeline is general and can be applied to identify pseudoknotted RNAs for targeted therapeutics in genomes or transcriptomes from any virus or organism.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

RNA Secondary Structures with Given Motif Specification: Combinatorics and Algorithms

The study of native motifs of RNA secondary structures helps us better understand the formation and eventually the functions of these molecules. Commonly known structural motifs include helices, hairpin loops, bulges, interior loops, exterior loops and multiloops. However, enumerative results and generating algorithms taking into account the joint distribution of these motifs are sparse. In this paper, we present progress on deriving such distributions employing a tree-bijection of RNA secondary structures obtained by Schmitt and Waterman and a novel rake decomposition of plane trees. The key feature of the latter is that the derived components encode motifs of the RNA secondary structures without pseudoknots associated with the plane trees very well. As an application, we present an algorithm (RakeSamp) generating uniformly random secondary structures without pseudoknots that satisfy fine motif specifications on the length and degree of various types of loops as well as helices.

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.

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.

Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User's Guide to the LandscapeFold Algorithm

Here we detail the LandscapeFold secondary structure prediction algorithm and how it is used. The algorithm was previously described and tested in (Kimchi O et al., Biophys J 117(3):520-532, 2019), though it was not named there. The algorithm directly enumerates all possible secondary structures into which up to two RNA or single-stranded DNA sequences can fold. It uses a polymer physics model to estimate the configurational entropy of structures including complex pseudoknots. We detail each of these steps and ways in which the user can adjust the algorithm as desired. The code is available on the GitHub repository https://github.com/ofer-kimchi/LandscapeFold .

Genomic Analysis of Non-B Nucleic Acids Structures in SARS-CoV-2: Potential Key Roles for These Structures in Mutability, Translation, and Replication?

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.

Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.

Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Understanding the complex and specific roles played by non-coding RNAs (ncRNAs), which comprise the bulk of the genome, is important for understanding virtually every hallmark of cancer. This large group of molecules plays pivotal roles in key regulatory mechanisms in various cellular processes. Regulatory mechanisms, mediated by long non-coding RNA (lncRNA) and RNA-binding protein (RBP) interactions, are well documented in several types of cancer. Their effects are enabled through networks affecting lncRNA and RBP stability, RNA metabolism including N⁶-methyladenosine (m⁶A) and alternative splicing, subcellular localization, and numerous other mechanisms involved in cancer. In this review, we discuss the reciprocal interplay between lncRNAs and RBPs and their involvement in epigenetic regulation via histone modifications, as well as their key role in resistance to cancer therapy. Other aspects of RBPs including their structural domains, provide a deeper knowledge on how lncRNAs and RBPs interact and exert their biological functions. In addition, current state-of-the-art knowledge, facilitated by machine and deep learning approaches, unravels such interactions in better details to further enhance our understanding of the field, and the potential to harness RNA-based therapeutics as an alternative treatment modality for cancer are discussed.

Interactions of amyloid coaggregates with biomolecules and its relevance to neurodegeneration

The aggregation of amyloidogenic proteins is a pathological hallmark of various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. In these diseases, oligomeric intermediates or toxic aggregates of amyloids cause neuronal damage and degeneration. Despite the substantial effort made over recent decades to implement therapeutic interventions, these neurodegenerative diseases are not yet understood at the molecular level. In many cases, multiple disease-causing amyloids overlap in a sole pathological feature or a sole disease-causing amyloid represents multiple pathological features. Various amyloid pathologies can coexist in the same brain with or without clinical presentation and may even occur in individuals without disease. From sparse data, speculation has arisen regarding the coaggregation of amyloids with disparate amyloid species and other biomolecules, which are the same characteristics that make diagnostics and drug development challenging. However, advances in research related to biomolecular condensates and structural analysis have been used to overcome some of these challenges. Considering the development of these resources and techniques, herein we review the cross-seeding of amyloidosis, for example, involving the amyloids amyloid β, tau, α-synuclein, and human islet amyloid polypeptide, and their cross-inhibition by transthyretin and BRICHOS. The interplay of nucleic acid-binding proteins, such as prions, TAR DNA-binding protein 43, fused in sarcoma/translated in liposarcoma, and fragile X mental retardation polyglycine, with nucleic acids in the pathology of neurodegeneration are also described, and we thereby highlight the potential clinical applications in central nervous system therapy.

Lessons Learned and Yet-to-Be Learned on the Importance of RNA Structure in SARS-CoV-2 Replication

SARS-CoV-2, the etiological agent responsible for the COVID-19 pandemic, is a member of the virus family Coronaviridae, known for relatively extensive (~30-kb) RNA genomes that not only encode for numerous proteins but are also capable of forming elaborate structures. As highlighted in this review, these structures perform critical functions in various steps of the viral life cycle, ultimately impacting pathogenesis and transmissibility. We examine these elements in the context of coronavirus evolutionary history and future directions for curbing the spread of SARS-CoV-2 and other potential human coronaviruses. While we focus on structures supported by a variety of biochemical, biophysical, and/or computational methods, we also touch here on recent evidence for novel structures in both protein-coding and noncoding regions of the genome, including an assessment of the potential role for RNA structure in the controversial finding of SARS-CoV-2 integration in “long COVID” patients. This review aims to serve as a consolidation of previous works on coronavirus and more recent investigation of SARS-CoV-2, emphasizing the need for improved understanding of the role of RNA structure in the evolution and adaptation of these human viruses.

Orchestrating ribosomal RNA folding during ribosome assembly

Construction of the eukaryotic ribosome is a complex process in which a nascent ribosomal RNA (rRNA) emerging from RNA Polymerase I hierarchically folds into a native three-dimensional structure. Modular assembly of individual RNA domains through interactions with ribosomal proteins and a myriad of assembly factors permit efficient disentanglement of the error-prone RNA folding process. Following these dynamic events, long-range tertiary interactions are orchestrated to compact rRNA. A combination of genetic, biochemical, and structural studies is now providing clues into how a nascent rRNA is transformed into a functional ribosome with high precision. With this essay, we aim to draw attention to the poorly understood process of establishing correct RNA tertiary contacts during ribosome formation.

Applicability of the nearest-neighbour model for pseudoknot RNAs

The validity of the nearest-neighbour (NN) model was verified in an RNA pseudoknot (PK) structure. The thermodynamic parameters of the second hairpin stem (S2) region, which separates the PK from a hairpin structure, were monitored using CD and UV melting. Different PKs with identical NN base pairs in the S2 region exhibited similar thermodynamic parameters, highlighting the validity of the NN model in this RNA tertiary structure motif.

KnotAli: informed energy minimization through the use of evolutionary information

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.

Thermodynamics of unfolding mechanisms of mouse mammary tumor virus pseudoknot from a coarse-grained loop-entropy model

Pseudoknotted RNA molecules play important biological roles that depend on their folded structure. To understand the underlying principles that determine their thermodynamics and folding/unfolding mechanisms, we carried out a study on a variant of the mouse mammary tumor virus pseudoknotted RNA (VPK), a widely studied model system for RNA pseudoknots. Our method is based on a coarse-grained discrete-state model and the algorithm of PK3D (pseudoknot structure predictor in three-dimensional space), with RNA loops explicitly constructed and their conformational entropic effects incorporated. Our loop entropy calculations are validated by accurately capturing previously measured melting temperatures of RNA hairpins with varying loop lengths. For each of the hairpins that constitutes the VPK, we identified alternative conformations that are more stable than the hairpin structures at low temperatures and predicted their populations at different temperatures. Our predictions were validated by thermodynamic experiments on these hairpins. We further computed the heat capacity profiles of VPK, which are in excellent agreement with available experimental data. Notably, our model provides detailed information on the unfolding mechanisms of pseudoknotted RNA. Analysis of the distribution of base-pairing probability of VPK reveals a cooperative unfolding mechanism instead of a simple sequential unfolding of first one stem and then the other. Specifically, we find a simultaneous "loosening" of both stems as the temperature is raised, whereby both stems become partially melted and co-exist during the unfolding process.

Development of RNA G-quadruplex (rG4)-targeting L-RNA aptamers by rG4-SELEX

RNA G-quadruplex (rG4)-SELEX is a method that generates L-RNA aptamers to target an rG4 structure of interest, which can be applied to inhibit G-quadruplex-mediated interactions that have important roles in gene regulation and function. Here we present a Protocol Extension substantially modifying an existing SELEX protocol to describe in detail the procedures involved in performing rG4-SELEX to identify rG4-specific binders that can effectively suppress rG4-peptide and rG4-protein associations. This Protocol Extension improves the speed of aptamer discovery and identification, offering a suite of techniques to characterize the aptamer secondary structure and monitor binding affinity and specificity, and demonstrating the utility of the L-RNA aptamer. The previous protocol mainly describes the identification of RNA aptamers against proteins of interest, whereas in this Protocol Extension we present the development of an unnatural RNA aptamer against an RNA structure of interest, with the potential to be applicable to other nucleic acid motifs or biomolecules. rG4-SELEX starts with a random D-RNA library incubated with the L-rG4 target of interest, followed by binding, washing and elution of the library. Enriched D-aptamer candidates are sequenced and structurally characterized. Then, the L-aptamer is synthesized and used for different applications. rG4-SELEX can be carried out by an experienced molecular biologist with a basic understanding of nucleic acids. The development of rG4-targeting L-RNA aptamers expands the current rG4 toolkit to explore innovative rG4-related applications, and opens new doors to discovering novel rG4 biology in the near future. The duration of each selection cycle as outlined in the protocol is ~2 d.

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition

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.

RNAlign2D: a rapid method for combined RNA structure and sequence-based alignment using a pseudo-amino acid substitution matrix

Background

The functions of RNA molecules are mainly determined by their secondary structures. These functions can also be predicted using bioinformatic tools that enable the alignment of multiple RNAs to determine functional domains and/or classify RNA molecules into RNA families. However, the existing multiple RNA alignment tools, which use structural information, are slow in aligning long molecules and/or a large number of molecules. Therefore, a more rapid tool for multiple RNA alignment may improve the classification of known RNAs and help to reveal the functions of newly discovered RNAs.

Results

Here, we introduce an extremely fast Python-based tool called RNAlign2D. It converts RNA sequences to pseudo-amino acid sequences, which incorporate structural information, and uses a customizable scoring matrix to align these RNA molecules via the multiple protein sequence alignment tool MUSCLE.

Conclusions

RNAlign2D produces accurate RNA alignments in a very short time. The pseudo-amino acid substitution matrix approach utilized in RNAlign2D is applicable for virtually all protein aligners.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04426-8.

Prediction of RNA secondary structure including pseudoknots for long sequences

RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is infeasible to construct an efficiently computable secondary structure model including pseudoknots, secondary structure prediction methods considering pseudoknots are not yet widely available. We developed IPknot, which uses heuristics to speed up computations, but it has remained difficult to apply it to long sequences, such as messenger RNA and viral RNA, because it requires cubic computational time with respect to sequence length and has threshold parameters that need to be manually adjusted. Here, we propose an improvement of IPknot that enables calculation in linear time by employing the LinearPartition model and automatically selects the optimal threshold parameters based on the pseudo-expected accuracy. In addition, IPknot showed favorable prediction accuracy across a wide range of conditions in our exhaustive benchmarking, not only for single sequences but also for multiple alignments.

Graph Theoretical Methods and Workflows for Searching and Annotation of RNA Tertiary Base Motifs and Substructures

The increasing number and complexity of structures containing RNA chains in the Protein Data Bank (PDB) have led to the need for automated structure annotation methods to replace or complement expert visual curation. This is especially true when searching for tertiary base motifs and substructures. Such base arrangements and motifs have diverse roles that range from contributions to structural stability to more direct involvement in the molecule's functions, such as the sites for ligand binding and catalytic activity. We review the utility of computational approaches in annotating RNA tertiary base motifs in a dataset of PDB structures, particularly the use of graph theoretical algorithms that can search for such base motifs and annotate them or find and annotate clusters of hydrogen-bond-connected bases. We also demonstrate how such graph theoretical algorithms can be integrated into a workflow that allows for functional analysis and comparisons of base arrangements and sub-structures, such as those involved in ligand binding. The capacity to carry out such automatic curations has led to the discovery of novel motifs and can give new context to known motifs as well as enable the rapid compilation of RNA 3D motifs into a database.

Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

In silico strategies for modeling RNA aptamers and predicting binding sites of their molecular targets

RNA aptamers are single-stranded nucleic acids of 20-100 nucleotides, with high sensitivity and specificity against particular molecular targets. In vitro production and selection of aptamers can be performed using the SELEX method. However, this procedure requires considerable time and cost. In this sense, bioinformatics tools play an important role in reducing the time and cost associated with development and production of aptamers. In this article, we propose bioinformatics strategies for modeling and analysis of the interaction with molecular targets for two RNA aptamers: ATP binding RNA aptamer and iSpinach aptamer. For this purpose, molecular modeling of the tertiary structure of the aptamers was performed with two servers (SimRNA and RNAComposer); and AutoDock Vina and rDock programs were used to dock their respective ligands. The predictions developed with these methods could be used for in silico design of RNA aptamers, through a simple and accessible methodology.Supplemental data for this article is available online at https://doi.org/10.1080/15257770.2021.1951754 .

The energy-spectrum of bicompatible sequences

Background

Genotype-phenotype maps provide a meaningful filtration of sequence space and RNA secondary structures are particular such phenotypes. Compatible sequences, which satisfy the base-pairing constraints of a given RNA structure, play an important role in the context of neutral evolution. Sequences that are simultaneously compatible with two given structures (bicompatible sequences), are beacons in phenotypic transitions, induced by erroneously replicating populations of RNA sequences. RNA riboswitches, which are capable of expressing two distinct secondary structures without changing the underlying sequence, are one example of bicompatible sequences in living organisms.

Results

We present a full loop energy model Boltzmann sampler of bicompatible sequences for pairs of structures. The sequence sampler employs a dynamic programming routine whose time complexity is polynomial when assuming the maximum number of exposed vertices, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ, is a constant. The parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ depends on the two structures and can be very large. We introduce a novel topological framework encapsulating the relations between loops that sheds light on the understanding of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ. Based on this framework, we give an algorithm to sample sequences with minimum \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ on a particular topologically classified case as well as giving hints to the solution in the other cases. As a result, we utilize our sequence sampler to study some established riboswitches.

Conclusion

Our analysis of riboswitch sequences shows that a pair of structures needs to satisfy key properties in order to facilitate phenotypic transitions and that pairs of random structures are unlikely to do so. Our analysis observes a distinct signature of riboswitch sequences, suggesting a new criterion for identifying native sequences and sequences subjected to evolutionary pressure. Our free software is available at: https://github.com/FenixHuang667/Bifold.

Post-transcriptional control of T-cell cytokine production: Implications for cancer therapy

As part of the adaptive immune system, T cells are vital for the eradication of infected and malignantly transformed cells. To perform their protective function, T cells produce effector molecules that are either directly cytotoxic, such as granzymes, perforin, interferon-γ and tumour necrosis factor α, or attract and stimulate (immune) cells, such as interleukin-2. As these molecules can also induce immunopathology, tight control of their production is required. Indeed, inflammatory cytokine production is regulated on multiple levels. Firstly, locus accessibility and transcription factor availability and activity determine the amount of mRNA produced. Secondly, post-transcriptional mechanisms, influencing mRNA splicing/codon usage, stability, decay, localization and translation rate subsequently determine the amount of protein that is produced. In the immune suppressive environments of tumours, T cells gradually lose the capacity to produce effector molecules, resulting in tumour immune escape. Recently, the role of post-transcriptional regulation in fine-tuning T-cell effector function has become more appreciated. Furthermore, several groups have shown that exhausted or dysfunctional T cells from cancer patients or murine models possess mRNA for inflammatory mediators, but fail to produce effector molecules, hinting that post-transcriptional events also play a role in hampering tumour-infiltrating lymphocyte effector function. Here, the post-transcriptional regulatory events governing T-cell cytokine production are reviewed, with a specific focus on the importance of post-transcriptional regulation in anti-tumour responses. Furthermore, potential approaches to circumvent tumour-mediated dampening of T-cell effector function through the (dis)engagement of post-transcriptional events are explored, such as CRISPR/Cas9-mediated genome editing or chimeric antigen receptors.

Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement

Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations. The results show that spatial confinements can not only enhance the compactness and stability of MMTV pseudoknot structures but also weaken the dependence of the RNA structure compactness and stability on salt concentration. Based on our microscopic analyses, we found that the effect of spatial confinement on the salt-dependent RNA pseudoknot stability mainly comes through the spatial suppression of extended conformations, which are prevalent in the partially/fully unfolded states, especially at low ion concentrations. Furthermore, our comprehensive analyses revealed that the thermally unfolding pathway of the pseudoknot can be significantly modulated by spatial confinements, since the intermediate states with more extended conformations would loss favor when spatial confinements are introduced.

Deep Learning Method for RNA Secondary Structure Prediction with Pseudoknots Based on Large-Scale Data

Traditional machine learning methods are widely used in the field of RNA secondary structure prediction and have achieved good results. However, with the emergence of large-scale data, deep learning methods have more advantages than traditional machine learning methods. As the number of network layers increases in deep learning, there will often be problems such as increased parameters and overfitting. We used two deep learning models, GoogLeNet and TCN, to predict RNA secondary results. And from the perspective of the depth and width of the network, improvements are made based on the neural network model, which can effectively improve the computational efficiency while extracting more feature information. We process the existing real RNA data through experiments, use deep learning models to extract useful features from a large amount of RNA sequence data and structure data, and then predict the extracted features to obtain each base’s pairing probability. The characteristics of RNA secondary structure and dynamic programming methods are used to process the base prediction results, and the structure with the largest sum of the probability of each base pairing is obtained, and this structure will be used as the optimal RNA secondary structure. We, respectively, evaluated GoogLeNet and TCN models based on 5sRNA, tRNA data, and tmRNA data, and compared them with other standard prediction algorithms. The sensitivity and specificity of the GoogLeNet model on the 5sRNA and tRNA data sets are about 16% higher than the best prediction results in other algorithms. The sensitivity and specificity of the GoogLeNet model on the tmRNA dataset are about 9% higher than the best prediction results in other algorithms. As deep learning algorithms’ performance is related to the size of the data set, as the scale of RNA data continues to expand, the prediction accuracy of deep learning methods for RNA secondary structure will continue to improve.

Toll-like receptor-2/7-mediated T cell activation: An innate potential to augment CD8+ T cell cytokine production

CD8⁺ T cells are critical to combat pathogens and eradicate malignantly transformed cells. To exert their effector function and kill target cells, T cells produce copious amounts of effector molecules, including the pro-inflammatory cytokines interferon γ, tumour necrosis factor α and interleukin 2. TCR triggering alone is sufficient to induce cytokine secretion by effector and memory CD8⁺ T cells. However, T cells can also be directly activated by pathogen-derived molecules, such as through the triggering of Toll-like receptors (TLRs). TLR-mediated pathogen sensing by T cells results in the production of only interferon γ. However, in particular when the antigen load on target cells is low, or when TCR affinity to the antigen is limited, antigen-experienced T cells can benefit from costimulatory signals. TLR stimulation can also function in a costimulatory fashion to enhance TCR triggering. Combined TCR and TLR triggering enhances the proliferation, memory formation and effector function of T cells, resulting in enhanced production of interferon γ, tumour necrosis factor α and interleukin 2. Therefore, TLR ligands or the exploitation of TLR signalling could provide novel opportunities for immunotherapy approaches. In fact, CD19 CAR T cells bearing an intracellular TLR2 costimulatory domain were recently employed to treat cancer patients in a clinical trial. Here, the current knowledge regarding TLR2/7 stimulation-induced cytokine production by T cells is reviewed. Specifically, the transcriptional and post-transcriptional pathways engaged upon TLR2/7 sensing and TLR2/7 signalling are discussed. Finally, the potential uses of TLRs to enhance the anti-tumor effector function of T cells are explored.

Computer-Aided Design of Active Pseudoknotted Hammerhead Ribozymes

Pseudoknots are important motifs for stabilizing the structure of functional RNAs. As an example, pseudoknotted hammerhead ribozymes are highly active compared to minimal ribozymes. The design of new RNA sequences that retain the function of a model RNA structure includes taking in account pseudoknots presence in the structure, which is usually a challenge for bioinformatics tools. Our method includes using "Enzymer," a software for designing RNA sequences with desired secondary structures that may include pseudoknots. Enzymer implements an efficient stochastic search and optimization algorithm to sample RNA sequences from low ensemble defect mutational landscape of an initial design template to generate an RNA sequence that is predicted to fold into the desired target structure.

Genome-scale molecular principles of mRNA half-life regulation in yeast

Precise control of protein and messenger RNA (mRNA) degradation is essential for cellular metabolism and homeostasis. Controlled and specific degradation of both molecular species necessitates their engagements with the respective degradation machineries; this engagement involves a disordered/unstructured segment of the substrate traversing the degradation tunnel of the machinery and accessing the catalytic sites. However, while molecular factors influencing protein degradation have been extensively explored on a genome scale, and in multiple organisms, such a comprehensive understanding remains missing for mRNAs. Here, we analyzed multiple genome-scale experimental yeast mRNA half-life data in light of experimentally derived mRNA secondary structures and protein binding data, along with high-resolution X-ray crystallographic structures of the RNase machines. Results unraveled a consistent genome-scale trend that mRNAs comprising longer terminal and/or internal unstructured segments have significantly shorter half-lives; the lengths of the 5'-terminal, 3'-terminal, and internal unstructured segments that affect mRNA half-life are compatible with molecular structures of the 5' exo-, 3' exo-, and endoribonuclease machineries. Sequestration into ribonucleoprotein complexes elongates mRNA half-life, presumably by burying ribonuclease engagement sites under oligomeric interfaces. After gene duplication, differences in terminal unstructured lengths, proportions of internal unstructured segments, and oligomerization modes result in significantly altered half-lives of paralogous mRNAs. Side-by-side comparison of molecular principles underlying controlled protein and mRNA degradation in yeast unravels their remarkable mechanistic similarities and suggests how the intrinsic structural features of the two molecular species, at two different levels of the central dogma, regulate their half-lives on genome scale.

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.

Tertiary Base Triple Formation in the SRV-1 Frameshifting Pseudoknot Stabilizes Secondary Structure Components

Minor-groove base triples formed between stem 1 and loop 2 of the simian retrovirus type 1 (SRV-1) mRNA frameshifting pseudoknot are essential in stimulating -1 ribosomal frameshifting. How tertiary base triple formation affects the local stabilities of secondary structures (stem 1 and stem 2) and thus ribosomal frameshifting efficiency is not well understood. We made a short peptide nucleic acid (PNA) that is expected to invade stem 1 of the SRV-1 pseudoknot by PNA-RNA duplex formation to mimic the stem 1 unwinding process by a translating ribosome. In addition, we used a PNA for invading stem 2 in the SRV-1 pseudoknot. Our nondenaturing polyacrylamide gel electrophoresis data for the binding of PNA to the SRV-1 pseudoknot and mutants reveal that mutations in loop 2 disrupting base triple formation between loop 2 and stem 1 in the SRV-1 pseudoknot result in enhanced invasion by both PNAs. Our data suggest that tertiary stem 1-loop 2 base triple interactions in the SRV-1 pseudoknot can stabilize both of the secondary structural components, stem 1 and stem 2. Stem 2 stability is thus coupled to the structural stability of stem 1-loop 2 base triples, mediated through a long-range effect. The apparent dissociation constants of both PNAs are positively correlated with the pseudoknot mechanical stabilities and frameshifting efficiencies. The relatively simple PNA local invasion experiment may be used to characterize the energetic contribution of tertiary interactions and ligand binding in many other RNA and DNA structures.

ASPRAlign: a tool for the alignment of RNA secondary structures with arbitrary pseudoknots

SUMMARY

Current methods for comparing RNA secondary structures are based on tree representations and exploit edit distance or alignment algorithms. Most of them can only process structures without pseudoknots. To overcome this limitation, we introduce ASPRAlign, a Java tool that aligns particular algebraic tree representations of RNA. These trees neglect the primary sequence and can handle structures with arbitrary pseudoknots. A measure of comparison, called ASPRA distance, is computed with a worst-case time complexity of O(n2) where n is the number of nucleotides of the longer structure.

AVAILABILITY AND IMPLEMENTATION

ASPRAlign is implemented in Java and source code is released under the GNU GPLv3 license. Code and documentation are freely available at https://github.com/bdslab/aspralign.

CONTACT

luca.tesei@unicam.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.