Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins

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 .

Thermostability, Tunability, and Tenacity of RNA as Rubbery Anionic Polymeric Materials in Nanotechnology and Nanomedicine-Specific Cancer Targeting with Undetectable Toxicity

RNA nanotechnology is the bottom-up self-assembly of nanometer-scale architectures, resembling LEGOs, composed mainly of RNA. The ideal building material should be (1) versatile and controllable in shape and stoichiometry, (2) spontaneously self-assemble, and (3) thermodynamically, chemically, and enzymatically stable with a long shelf life. RNA building blocks exhibit each of the above. RNA is a polynucleic acid, making it a polymer, and its negative-charge prevents nonspecific binding to negatively charged cell membranes. The thermostability makes it suitable for logic gates, resistive memory, sensor set-ups, and NEM devices. RNA can be designed and manipulated with a level of simplicity of DNA while displaying versatile structure and enzyme activity of proteins. RNA can fold into single-stranded loops or bulges to serve as mounting dovetails for intermolecular or domain interactions without external linking dowels. RNA nanoparticles display rubber- and amoeba-like properties and are stretchable and shrinkable through multiple repeats, leading to enhanced tumor targeting and fast renal excretion to reduce toxicities. It was predicted in 2014 that RNA would be the third milestone in pharmaceutical drug development. The recent approval of several RNA drugs and COVID-19 mRNA vaccines by FDA suggests that this milestone is being realized. Here, we review the unique properties of RNA nanotechnology, summarize its recent advancements, describe its distinct attributes inside or outside the body and discuss potential applications in nanotechnology, medicine, and material science.

Multipolar electrostatics for hairpin and pseudoknots in RNA: Improving the accuracy of force field potential energy function

Molecular dynamics (MD) simulations that rely on force field methods has been widely used to explore the structure and function of RNAs. However, the current commonly used force fields are limited by the electrostatic description offered by atomic charge, dipole and at most quadrupole moments, failing to capture the anisotropic picture of electronic features. Actually, the distribution of electrons around atomic nuclei is not spherically symmetric but is geometry dependent. A multipolar electrostatic model based on high rank multipole moments is described in this work, which allows us to combine polarizability and anisotropy of electron density. RNA secondary structure was taken as a research system, and its substructures including stem, loops (hairpin loop, bulge loop, internal loop, and multi-branch loop), and pseudoknots (H-type and K-type) were investigated, respectively, as well as the hairpin. First, the atom-atom electrostatic properties derived from one chain of a duplex RNA 2MVY in our previous work (Ref. 58) were measured by the pilot RNA systems of hairpin, hairpin loop, stem, and H-type pseudoknot, respectively. The prediction results were not satisfactory. Consequently, to obtain a general set of electrostatic parameters for RNA force fields, the convergence behavior of the atom-atom electrostatic interactions in the pilot RNA systems was explored using high rank atomic multipole moments. The pilot RNA systems were cut into four types of different-sized molecular fragments, and the single nucleotide fragment and nucleotide-paired fragment proved to be the most reasonable systems for base-unpairing regions and base-pairing regions to investigate the convergence behavior of all types of atom-atom electrostatic interactions, respectively. Transferability of the electrostatic properties drawn from the pilot RNA systems to the corresponding test systems was also investigated. Furthermore, the convergence behavior of atomic electrostatic interactions in other substructures including bulge loop, internal loop, multi-branch loop, and K-type pseudoknot was expected to be modeled via the hairpin.

RNAknot: A new algorithm for RNA secondary structure prediction based on genetic algorithm and GRASP method

The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot classes, but only a few of them have reached satisfying results in terms of both complexity and accuracy. Here we present RNAknot, a new method for predicting RNA secondary structure that contains the following components: stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, and internal loops, in addition to two types of pseudoknots, H-type pseudoknot and Hairpin kissing. RNAknot is based on a genetic algorithm and Greedy Randomized Adaptive Search Procedure (GRASP), and it uses the free energy as fitness function to evaluate the obtained structures. In order to validate the performance of the presented method 131 tests have been performed using two datasets of 26 and 105 RNA sequences, which have been taken from the two data bases RNAstrand and Pseudobase respectively. The obtained results are compared with those of some RNA secondary structure prediction programs such as Vs_subopt, CyloFold, IPknot, Kinefold, RNAstructure, and Sfold. The results of this comparative study show that the prediction accuracy of our proposed approach is significantly improved compared to those obtained by the other programs. For the first dataset, RNAknot has the highest specificity (SP) (71.23%) and sensitivity (SN) (72.15%) averages compared to the other programs. Concerning the second dataset, the RNA secondary structure predictions obtained by the RNAknot correspond to the highest averages of SP (85.49%) and F-measure (79.97%) compared to the other programs. The program is available as a jar file in the link: www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar.

New opportunities for designing effective small interfering RNAs

Small interfering RNAs (siRNAs) that silence genes of infectious diseases are potentially potent drugs. A continuing obstacle for siRNA-based drugs is how to improve their efficacy for adequate dosage. To overcome this obstacle, the interactions of antiviral siRNAs, tested in vivo, were computationally examined within the RNA-induced silencing complex (RISC). Thermodynamics data show that a persistent RISC cofactor is significantly more exothermic for effective antiviral siRNAs than their ineffective counterparts. Detailed inspection of viral RNA secondary structures reveals that effective antiviral siRNAs target hairpin or pseudoknot loops. These structures are critical for initial RISC interactions since they partially lack intramolecular complementary base pairing. Importing two temporary RISC cofactors from magnesium-rich hairpins and/or pseudoknots then kickstarts full RNA hybridization and hydrolysis. Current siRNA design guidelines are based on RNA primary sequence data. Herein, the thermodynamics of RISC cofactors and targeting magnesium-rich RNA secondary structures provide additional guidelines for improving siRNA design.

Defective RNA of a Novel Mycovirus with High Transmissibility Detrimental to Biocontrol Properties of Trichoderma spp

Trichoderma species are a group of fungi which is widely distributed in major terrestrial ecosystems; they are also commonly used as biocontrol agents for many plant diseases. A virus, namely Trichoderma harzianum hypovirus 1 (ThHV1), was identified in T. harzianum isolate T-70, and also infected isolate T-70D, together with its defective RNA (ThHV1-S). The ThHV1 genome possessed two Open Reading Frames (ORFs), namely ORF1 and ORF2. The start codon of ORF2 overlapped with the stop codon of ORF1 in a 43 nt long region. The polypeptide encoded by ORF2 of ThHV1 shared sequence similarities with those of betahypoviruses, indicating that ThHV1 is a novel member of Hypoviridea. Isolate T-70D, carrying both ThHV1 and ThHV1-S, showed abnormal biological properties, notably a decreased mycoparasitism ability when compared with isolate T-70. Both ThHV1 and ThHV1-S could be vertically transmitted to conidia and horizontally transmitted to T. harzianum isolate T-68 and T. koningiopsis T-51. The derivative strains carrying both ThHV1 and ThHV1-S showed decreased mycoparasitism ability, whereas strains carrying ThHV1 alone were normal, indicating that ThHV1-S is closely associated with the decreased mycoparasitism ability of T. harzianum isolate T-70D. ThHV1 was widely detected in isolates of T. harzianum, T. koningiopsis and T. atroviride originating from soil of China. Therefore, viruses in fungal biocontrol agents may also be a factor associated with the stability of their application.

A Polymer Physics Framework for the Entropy of Arbitrary Pseudoknots

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past 40 years. Although many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges arising from two main factors: 1) no physical model exists to estimate the loop entropies of complex intramolecular pseudoknots, and 2) their NP-complete enumeration has impeded their study. Here, we address both challenges. First, we develop a polymer physics model that can address arbitrarily complex pseudoknots using only two parameters corresponding to concrete physical quantities-over an order of magnitude fewer than the sparsest state-of-the-art phenomenological methods. Second, by coupling this model to exhaustive enumeration of the set of possible structures, we compute the entire free energy landscape of secondary structures resulting from a primary RNA sequence. We demonstrate that for RNA structures of ∼80 nucleotides, with minimal heuristics, the complete enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem. We further show that despite our loop entropy model's parametric sparsity, it performs better than or on par with previously published methods in predicting both pseudoknotted and non-pseudoknotted structures on a benchmark data set of RNA structures of ≤80 nucleotides. We suggest ways in which the accuracy of the model can be further improved.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, ‘bpRNA-1m’, of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

Hiding in plain sight: New virus genomes discovered via a systematic analysis of fungal public transcriptomes

The distribution and diversity of RNA viruses in fungi is incompletely understood due to the often cryptic nature of mycoviral infections and the focused study of primarily pathogenic and/or economically important fungi. As most viruses that are known to infect fungi possess either single-stranded or double-stranded RNA genomes, transcriptomic data provides the opportunity to query for viruses in diverse fungal samples without any a priori knowledge of virus infection. Here we describe a systematic survey of all transcriptomic datasets from fungi belonging to the subphylum Pezizomycotina. Using a simple but effective computational pipeline that uses reads discarded during normal RNA-seq analyses, followed by identification of a viral RNA-dependent RNA polymerase (RdRP) motif in de novo assembled contigs, 59 viruses from 44 different fungi were identified. Among the viruses identified, 88% were determined to be new species and 68% are, to our knowledge, the first virus described from the fungal species. Comprehensive analyses of both nucleotide and inferred protein sequences characterize the phylogenetic relationships between these viruses and the known set of mycoviral sequences and support the classification of up to four new families and two new genera. Thus the results provide a deeper understanding of the scope of mycoviral diversity while also increasing the distribution of fungal hosts. Further, this study demonstrates the suitability of analyzing RNA-seq data to facilitate rapid discovery of new viruses.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model

Motivation

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method.

Results

The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

Over-expression of a human CD62L ecto-domain and a potential role of RNA pseudoknot structures in recombinant protein expression

L-selectin (CD62L) is an extracellular protein with a lectin-like domain that mediates rolling adhesion of leukocytes to vascular endothelial cell surfaces. Currently, there are no solved structures for the ectodomain of CD62L, nor of CD62L in complex with its ligand. We have developed a rapid mammalian recombinant protein expression system using an amplifiable glutamine synthase based vector. Here, we further developed and applied this method to express and purify the entire extracellular region of CD62L. This resulted in excess of 20 mg/L yield of recombinant CD62L. In an attempt to understand the different expression levels among four similar CD62L constructs that differ primarily in signal sequences, we calculated the presence of potential RNA pseudoknots in their signal sequences. The results showed the presence of pseudoknots involving the start codon and between the signal sequence and gene in the mRNA of the non-expressing constructs, suggesting a potential inhibitory role of RNA pseudoknots in recombinant protein expression.

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

VfoldCPX Server: Predicting RNA-RNA Complex Structure and Stability

RNA-RNA interactions are essential for genomic RNA dimerization, mRNA splicing, and many RNA-related gene expression and regulation processes. The prediction of the structure and folding stability of RNA-RNA complexes is a problem of significant biological importance and receives substantial interest in the biological community. The VfoldCPX server provides a new web interface to predict the two-dimensional (2D) structures of RNA-RNA complexes from the nucleotide sequences. The VfoldCPX server has several novel advantages including the ability to treat RNAs with tertiary contacts (crossing base pairs) such as loop-loop kissing interactions and the use of physical loop entropy parameters. Based on a partition function-based algorithm, the server enables prediction for structure with and without tertiary contacts. Furthermore, the server outputs a set of energetically stable structures, ranked by their stabilities. The results allow users to gain extensive physical insights into RNA-RNA interactions and their roles in RNA function. The web server is freely accessible at “http://rna.physics.missouri.edu/vfoldCPX”.

Computational study of stability of an H-H-type pseudoknot motif

Motifs in RNA tertiary structures are important to their structural organizations and biological functions. Here we consider an H-H-type pseudoknot (HHpk) motif that consists of two hairpins connected by a junction loop and with kissing interactions between the two hairpin loops. Such a tertiary structural motif is recurrently found in RNA tertiary structures, but is difficult to predict computationally. So it is important to understand the mechanism of its formation and stability. Here we investigate the stability of the HHpk tertiary structure by using an all-atom molecular dynamics simulation. The results indicate that the HHpk tertiary structure is stable. However, it is found that this stability is not due to the helix-helix packing, as is usually expected, but is maintained by the combined action of the kissing hairpin loops and junctions, although the former plays the main role. Stable HHpk motifs may form structural platforms for the molecules to realize their biological functions. These results are useful for understanding the construction principle of RNA tertiary structures and structure prediction.

Physics-based RNA structure prediction

Despite the success of RNA secondary structure prediction for simple, short RNAs, the problem of predicting RNAs with long-range tertiary folds remains. Furthermore, RNA 3D structure prediction is hampered by the lack of the knowledge about the tertiary contacts and their thermodynamic parameters. Low-resolution structural modeling enables us to estimate the conformational entropies for a number of tertiary folds through rigorous statistical mechanical calculations. The models lead to 3D tertiary folds at coarse-grained level. The coarse-grained structures serve as the initial structures for all-atom molecular dynamics refinement to build the final all-atom 3D structures. In this paper, we present an overview of RNA computational models for secondary and tertiary structures’ predictions and then focus on a recently developed RNA statistical mechanical model—the Vfold model. The main emphasis is placed on the physics behind the models, including the treatment of the non-canonical interactions in secondary and tertiary structure modelings, and the correlations to RNA functions.

Predicting RNA 3D structure using a coarse-grain helix-centered model

A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures.

Predicting structure and stability for RNA complexes with intermolecular loop-loop base-pairing

RNA loop-loop interactions are essential for genomic RNA dimerization and regulation of gene expression. In this article, a statistical mechanics-based computational method that predicts the structures and thermodynamic stabilities of RNA complexes with loop-loop kissing interactions is described. The method accounts for the entropy changes for the formation of loop-loop interactions, which is a notable advancement that other computational models have neglected. Benchmark tests with several experimentally validated systems show that the inclusion of the entropy parameters can indeed improve predictions for RNA complexes. Furthermore, the method can predict not only the native structures of RNA/RNA complexes but also alternative metastable structures. For instance, the model predicts that the SL1 domain of HIV-1 RNA can form two different dimer structures with similar stabilities. The prediction is consistent with experimental observation. In addition, the model predicts two different binding sites for hTR dimerization: One binding site has been experimentally proposed, and the other structure, which has a higher stability, is structurally feasible and needs further experimental validation.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Background

Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information.

Results

We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0.

Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure.

Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets.

Conclusions

Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php.

Characterization of Dak Nong virus, an insect nidovirus isolated from Culex mosquitoes in Vietnam

In this study, we isolated and characterized an insect nidovirus from the mosquito Culex tritaeniorhynchus Giles (Diptera: Culicidae) in Vietnam, as an additional member of the new family Mesoniviridae in the order Nidovirales. The virus, designated “Dak Nong virus (DKNV),” shared many characteristics with Cavally virus and Nam Dinh virus, which have also been discovered recently in mosquitoes, and these viruses should be considered members of a single virus species, Alphamesonivirus 1. DKNV grew in cultured mosquito cells but could not replicate in the cultured vertebrate cells tested. N-terminal sequencing of the DKNV structural proteins revealed two posttranslational cleavage sites in the spike glycoprotein precursor. DKNV is assumed to be a new member of the species Alphamesonivirus 1, and the current study provides further understanding of viruses belonging to the new family Mesoniviridae.

Predicting pseudoknotted structures across two RNA sequences

Motivation: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity.

Results: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support.

Availability: DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw.

Contact:janaspe@csse.uwa.edu.au

Supplementary information:Supplementary data are available at Bioinformatics online.

Prevalence and diversity of viruses in the entomopathogenic fungus Beauveria bassiana

Viruses have been discovered in numerous fungal species, but unlike most known animal or plant viruses, they are rarely associated with deleterious effects on their hosts. The knowledge about viruses among entomopathogenic fungi is very limited, although their existence is suspected because of the presence of virus-like double-stranded RNA (dsRNA) in isolates of several species. Beauveria bassiana is one of the most-studied species of entomopathogenic fungi; it has a cosmopolitan distribution and is used as a biological control agent against invertebrates in agriculture. We analyzed a collection of 73 isolates obtained at different locations and from different habitats in Spain and Portugal, searching for dsRNA elements indicative of viral infections. The results revealed that the prevalence of viral infections is high; 54.8% of the isolates contained dsRNA elements with viral characteristics. The dsRNA electropherotypes of infected isolates indicated that virus diversity was high in the collection analyzed and that mixed virus infections occurred in fungal isolates. However, a hybridization experiment indicated that dsRNA bands that are similar in size do not always have similar sequences. Particular virus species or dsRNA profiles were not associated with locations or types of habitats, probably because of the ubiquity and efficient dispersion of this fungus as an airborne species. The sequence of one of the most common dsRNA elements corresponded to the 5.2-kbp genome of a previously undescribed member of the Totiviridae family, termed B. bassiana RNA virus 1 (BbRV1).

Molecular characterization of totiviruses in Xanthophyllomyces dendrorhous

Background

Occurrence of extrachromosomal dsRNA elements has been described in the red-yeast Xanthophyllomyces dendrorhous, with numbers and sizes that are highly variable among strains with different geographical origin. The studies concerning to the encapsidation in viral-like particles and dsRNA-curing have suggested that some dsRNAs are helper viruses, while others are satellite viruses. However, the nucleotide sequences and functions of these dsRNAs are still unknown. In this work, the nucleotide sequences of four dsRNAs of the strain UCD 67–385 of X. dendrorhous were determined, and their identities and genome structures are proposed. Based on this molecular data, the dsRNAs of different strains of X. dendrorhous were analyzed.

Results

The complete sequences of L1, L2, S1 and S2 dsRNAs of X. dendrorhous UCD 67–385 were determined, finding two sequences for L1 dsRNA (L1A and L1B). Several ORFs were uncovered in both S1 and S2 dsRNAs, but no homologies were found for any of them when compared to the database. Instead, two ORFs were identified in each L1A, L1B and L2 dsRNAs, whose deduced amino acid sequences were homologous with a major capsid protein (5’-ORF) and a RNA-dependent RNA polymerase (3’-ORF) belonging to the Totiviridae family. The genome structures of these dsRNAs are characteristic of Totiviruses, with two overlapped ORFs (the 3’-ORF in the −1 frame with respect to the 5’-ORF), with a slippery site and a pseudoknot in the overlapped regions. These structures are essential for the synthesis of the viral polymerase as a fusion protein with the viral capsid protein through −1 ribosomal frameshifting. In the RNase protection analysis, all the dsRNAs in the four analyzed X. dendrorhous strains were protected from enzymatic digestion. The RT-PCR analysis revealed that, similar to strain UCD 67–385, the L1A and L1B dsRNAs coexist in the strains VKM Y-2059, UCD 67–202 and VKM Y-2786. Furthermore, determinations of the relative amounts of L1 dsRNAs using two-step RT-qPCR revealed a 40-fold increment of the ratio L1A/L1B in the S2 dsRNA-cured strain compared to its parental strain.

Conclusions

Three totiviruses, named as XdV-L1A, XdV-L1B and XdV-L2, were identified in the strain UCD 67–385 of X. dendrorhous. The viruses XdV-L1A and XdV-L1B were also found in other three X. dendrorhous strains. Our results suggest that the smaller dsRNAs (named XdRm-S1 and XdRm-S2) of strain UCD 67–385 are satellite viruses, and particularly that XdRm-S2 is a satellite of XdV-L1A.

Conformational features of topologically classified RNA secondary structures

Background

Current RNA secondary structure prediction approaches predict prevalent pseudoknots such as the H-pseudoknot and kissing hairpin. The number of possible structures increases drastically when more complex pseudoknots are considered, thus leading to computational limitations. On the other hand, the enormous population of possible structures means not all of them appear in real RNA molecules. Therefore, it is of interest to understand how many of them really exist and the reasons for their preferred existence over the others, as any new findings revealed by this study might enhance the capability of future structure prediction algorithms for more accurate prediction of complex pseudoknots.

Methodology/Principal Findings

A novel algorithm was devised to estimate the exact number of structural possibilities for a pseudoknot constructed with a specified number of base pair stems. Then, topological classification was applied to classify RNA pseudoknotted structures from data in the RNA STRAND database. By showing the vast possibilities and the real population, it is clear that most of these plausible complex pseudoknots are not observed. Moreover, from these classified motifs that exist in nature, some features were identified for further investigation. It was found that some features are related to helical stacking. Other features are still left open to discover underlying tertiary interactions.

Conclusions

Results from topological classification suggest that complex pseudoknots are usually some well-known motifs that are themselves complex or the interaction results of some special motifs. Heuristics can be proposed to predict the essential parts of these complex motifs, even if the required thermodynamic parameters are currently unknown.

Predicting kissing interactions in microRNA-target complex and assessment of microRNA activity

MicroRNAs (miRNAs) are a class of short RNA molecules that play an important role in post-transcriptional gene regulation. Computational prediction of the miRNA target sites in mRNA is crucial for understanding the mechanism of miRNA-mRNA interactions. We here develop a new computational model that allows us to treat a variety of miRNA-mRNA kissing interactions, which have been ignored in the currently existing miRNA target prediction algorithms. By including all the different inter- and intra-molecular base pairs, this new model can predict both the structural accessibility of the target sites and the binding affinity (free energy). Applications of the model to a test set of 105 miRNA-gene systems show a notably improved success rate of 83/105. We found that although the binding affinity alone predicts the miRNA repression efficiency with a high success rate of 73/105, the structure in the seed region can significantly influence the miRNA activity. The method also allows us to efficiently search for the potent miRNA from a pool of miRNA candidates for any given gene target. Furthermore, extension of the method may enable predictions of the three-dimensional (3D) structures of miRNA/mRNA complexes.

A domain-based model for predicting large and complex pseudoknotted structures

Pseudoknotted structures play important structural and functional roles in RNA cellular functions at the level of transcription, splicing and translation. However, the problem of computational prediction for large pseudoknotted folds remains. Here we develop a domain-based method for predicting complex and large pseudoknotted structures from RNA sequences. The model is based on the observation that large RNAs can be separated into different structural domains. The basic idea is to first identify the domains and then predict the structures for each domain. Assembly of the domain structures gives the full structure. The use of the domain-based approach leads to a reduction of computational time by a factor of about ~N ( 2) for an N-nt sequence. As applications of the model, we predict structures for a variety of RNA systems, such as regions in human telomerase RNA (hTR), internal ribosome entry site (IRES) and HIV genome. The lengths of these sequences range from 200-nt to 400-nt. The results show good agreements with the experiments.

Structure and stability of RNA/RNA kissing complex: with application to HIV dimerization initiation signal

We develop a statistical mechanical model to predict the structure and folding stability of the RNA/RNA kissing-loop complex. One of the key ingredients of the theory is the conformational entropy for the RNA/RNA kissing complex. We employ the recently developed virtual bond-based RNA folding model (Vfold model) to evaluate the entropy parameters for the different types of kissing loops. A benchmark test against experiments suggests that the entropy calculation is reliable. As an application of the model, we apply the model to investigate the structure and folding thermodynamics for the kissing complex of the HIV-1 dimerization initiation signal. With the physics-based energetic parameters, we compute the free energy landscape for the HIV-1 dimer. From the energy landscape, we identify two minimal free energy structures, which correspond to the kissing-loop dimer and the extended-duplex dimer, respectively. The results support the two-step dimerization process for the HIV-1 replication cycle. Furthermore, based on the Vfold model and energy minimization, the theory can predict the native structure as well as the local minima in the free energy landscape. The root-mean-square deviations (RMSDs) for the predicted kissing-loop dimer and extended-duplex dimer are ~3.0 Å. The method developed here provides a new method to study the RNA/RNA kissing complex.

RNA sequence determinants of a coupled termination-reinitiation strategy for downstream open reading frame translation in Helminthosporium victoriae virus 190S and other victoriviruses (Family Totiviridae)

The genome-length, dicistronic mRNA of the double-stranded RNA fungal virus Helminthosporium victoriae virus 190S (genus Victorivirus, family Totiviridae) contains two long open reading frames (ORFs) that overlap in the tetranucleotide AUGA. Translation of the downstream ORF, which encodes the RNA-dependent RNA polymerase (RdRp), has been proposed to depend on ribosomal reinitiation following termination of the upstream ORF, which encodes the capsid protein. In the current study, we examined the RNA sequence determinants for RdRp translation in this virus and demonstrated that a coupled termination-reinitiation (stop-restart) strategy is indeed used. Signals for termination-reinitiation are found within a 32-nucleotide stretch of RNA immediately upstream of the AUGA motif, including a predicted pseudoknot structure. The close proximity in which this predicted structure is followed by the upstream ORF's stop codon appears to be especially important for promoting translation of the downstream ORF. The normal strong preferences for an AUG start codon and the canonical sequence context to favor translation initiation appear somewhat relaxed for the downstream ORF. Similar sequence motifs and predicted RNA structures in other victoriviruses suggest that they all share a related stop-restart strategy for RdRp translation. Members of the genus Victorivirus thus provide new and unique opportunities for exploring the molecular mechanisms of translational coupling, which remain only partly understood in this and other systems.

Structural basis of differential ligand recognition by two classes of bis-(3'-5')-cyclic dimeric guanosine monophosphate-binding riboswitches

The bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) signaling pathway regulates biofilm formation, virulence, and other processes in many bacterial species and is critical for their survival. Two classes of c-di-GMP-binding riboswitches have been discovered that bind this second messenger with high affinity and regulate diverse downstream genes, underscoring the importance of RNA receptors in this pathway. We have solved the structure of a c-di-GMP-II riboswitch, which reveals that the ligand is bound as part of a triplex formed with a pseudoknot. The structure also shows that the guanine bases of c-di-GMP are recognized through noncanonical pairings and that the phosphodiester backbone is not contacted by the RNA. Recognition is quite different from that observed in the c-di-GMP-I riboswitch, demonstrating that at least two independent solutions for RNA second messenger binding have evolved. We exploited these differences to design a c-di-GMP analog that selectively binds the c-di-GMP-II aptamer over the c-di-GMP-I RNA. There are several bacterial species that contain both types of riboswitches, and this approach holds promise as an important tool for targeting one riboswitch, and thus one gene, over another in a selective fashion.