RNA Secondary Structure Prediction Research Articles

RGRNA: prediction of RNA secondary structure based on replacement and growth of stems

Owing to their structural diversity, RNAs perform many diverse biological functions in the cell. RNA secondary structure is thus important for predicting RNA function. Here, we propose a new combinatorial optimization algorithm, named RGRNA, to improve the accuracy of predicting RNA secondary structure. Following the establishment of a stempool, the stems are sorted by length, and chosen from largest to smallest. If the stem selected is the true stem, the secondary structure of this stem when combined with another stem selected at random will have low free energy, and the free energy will tend to gradually diminish. The free energy is considered as a parameter and the structure is converted into binary numbers to determine stem compatibility, for step-by-step prediction of the secondary structure for all combinations of stems. The RNA secondary structure can be predicted by the RGRNA method. Our experimental results show that the proposed algorithm outperforms RNAfold in terms of sensitivity, specificity, and Matthews correlation coefficient value.

RNA secondary structure and nucleotide composition of the conserved hallmark sequence of Leishmania SIDER2 retroposons are essential for endonucleolytic cleavage and mRNA degradation.

We have reported previously that Short Interspersed Degenerate Retroposons of the SIDER2 subfamily, largely located within 3'UTRs of Leishmania transcripts, promote rapid turnover of mRNAs through endonucleolytic cleavage within the highly conserved second tandem 79-nt hallmark sequence (79-nt SII). Here, we used site-directed mutagenesis and in silico RNA structural studies to delineate the cis-acting requirements within 79-nt SII for cleavage and mRNA degradation. The putative cleavage site(s) and other nucleotides predicted to alter the RNA secondary structure of 79-nt SII were either deleted or mutated and their effect on mRNA turnover was monitored using a gene reporter system. We found that short deletions of 8-nt spanning the two predicted cleavage sites block degradation of SIDER2-containing transcripts, leading to mRNA accumulation. Furthermore, single or double substitutions of the dinucleotides targeted for cleavage as well as mutations altering the predicted RNA secondary structure encompassing both cleavage sites also prevent mRNA degradation, confirming that these dinucleotides are the bona fide cleavage sites. In line with these results, we show that stage-regulated SIDER2 inactivation correlates with the absence of endonucleolytic cleavage. Overall, these data demonstrate that both cleavage sites within the conserved 79-nt SII as well as RNA folding in this region are essential for SIDER2-mediated mRNA decay, and further support that SIDER2-harboring transcripts are targeted for degradation by endonucleolytic cleavage.

Modeling RNA Secondary Structure with Sequence Comparison and Experimental Mapping Data

Secondary structure prediction is an important problem in RNA bioinformatics because knowledge of structure is critical to understanding the functions of RNA sequences. Significant improvements in prediction accuracy have recently been demonstrated though the incorporation of experimentally obtained structural information, for instance using selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) mapping. However, such mapping data is currently available only for a limited number of RNA sequences. In this article, we present a method for extending the benefit of experimental mapping data in secondary structure prediction to homologous sequences. Specifically, we propose a method for integrating experimental mapping data into a comparative sequence analysis algorithm for secondary structure prediction of multiple homologs, whereby the mapping data benefits not only the prediction for the specific sequence that was mapped but also other homologs. The proposed method is realized by modifying the TurboFold II algorithm for prediction of RNA secondary structures to utilize basepairing probabilities guided by SHAPE experimental data when such data are available. The SHAPE-mapping-guided basepairing probabilities are obtained using the RSample method. Results demonstrate that the SHAPE mapping data for a sequence improves structure prediction accuracy of other homologous sequences beyond the accuracy obtained by sequence comparison alone (TurboFold II). The updated version of TurboFold II is freely available as part of the RNAstructure software package.

Corrigendum: RNA-MoIP: prediction of RNA secondary structure and local 3D motifs from sequence data.

Corrigendum: RNA-MoIP: prediction of RNA secondary structure and local 3D motifs from sequence data.

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be “ill conditioned” in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling’s improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones.

Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best

Algorithmic prediction of RNA secondary structure has been an area of active inquiry since the 1970s. Despite many innovations since then, our best techniques are not yet perfect. The workhorses of the RNA secondary structure prediction engine are recursions first described by Zuker and Stiegler in 1981. These have well understood caveats; a notable flaw is the ad-hoc treatment of multi-loops, also called helical-junctions, that persists today. While several advanced models for multi-loops have been proposed, it seems to have been assumed that incorporating them into the recursions would lead to intractability, and so no algorithms for these models exist. Some of these models include the classical model based on Jacobson–Stockmayer polymer theory, and another by Aalberts and Nadagopal that incorporates two-length-scale polymer physics. We have realized practical, tractable algorithms for each of these models. However, after implementing these algorithms, we found that no advanced model was better than the original, ad-hoc model used for multi-loops. While this is unexpected, it supports the praxis of the current model.

The effects of structure on pre-mRNA processing and stability.

Pre-mRNA molecules can form a variety of structures, and both secondary and tertiary structures have important effects on processing, function and stability of these molecules. The prediction of RNA secondary structure is a challenging problem and various algorithms that use minimum free energy, maximum expected accuracy and comparative evolutionary based methods have been developed to predict secondary structures. However, these tools are not perfect, and this remains an active area of research. The secondary structure of pre-mRNA molecules can have an enhancing or inhibitory effect on pre-mRNA splicing. An example of enhancing structure can be found in a novel class of introns in zebrafish. About 10% of zebrafish genes contain a structured intron that forms a bridging hairpin that enforces correct splice site pairing. Negative examples of splicing include local structures around splice sites that decrease splicing efficiency and potentially cause mis-splicing leading to disease. Splicing mutations are a frequent cause of hereditary disease. The transcripts of disease genes are significantly more structured around the splice sites, and point mutations that increase the local structure often cause splicing disruptions. Post-splicing, RNA secondary structure can also affect the stability of the spliced intron and regulatory RNA interference pathway intermediates, such as pre-microRNAs. Additionally, RNA secondary structure has important roles in the innate immune defense against viruses. Finally, tertiary structure can also play a large role in pre-mRNA splicing. One example is the G-quadruplex structure, which, similar to secondary structure, can either enhance or inhibit splicing through mechanisms such as creating or obscuring RNA binding protein sites.

JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures.

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html.

FledFold: A Novel Software for RNA Secondary Structure Prediction.

Background: RNA secondary structure is essential to understand the mechanism of RNAs.Method: In this paper, fledFold, a novel software for RNA secondary structure prediction, is introduced. It combines both thermodynamic and kinetic factors of RNA secondary structures and can predict RNA secondary structures from their primary sequences with local personal computers.Results: FledFold is implemented in C++ under Windows 7 and could run on windows 7 or later version with at least 2 GB of RAM. Fledfold is user friendly and could output results with multiple formats.Conslusion: FledFold will be a valuable tool for RNA researches and it could be downloaded freely from http://www.jlucomputer.com/fledfold.php

RNA-MoIP: prediction of RNA secondary structure and local 3D motifs from sequence data.

RNA structures are hierarchically organized. The secondary structure is articulated around sophisticated local three-dimensional (3D) motifs shaping the full 3D architecture of the molecule. Recent contributions have identified and organized recurrent local 3D motifs, but applications of this knowledge for predictive purposes is still in its infancy. We recently developed a computational framework, named RNA-MoIP, to reconcile RNA secondary structure and local 3D motif information available in databases. In this paper, we introduce a web service using our software for predicting RNA hybrid 2D–3D structures from sequence data only. Optionally, it can be used for (i) local 3D motif prediction or (ii) the refinement of user-defined secondary structures. Importantly, our web server automatically generates a script for the MC-Sym software, which can be immediately used to quickly predict all-atom RNA 3D models. The web server is available at http://rnamoip.cs.mcgill.ca.

In silico methods for co-transcriptional RNA secondary structure prediction and for investigating alternative RNA structure expression.

RNA transcripts are the primary products of active genes in any living organism, including many viruses. Their cellular destiny not only depends on primary sequence signals, but can also be determined by RNA structure. Recent experimental evidence shows that many transcripts can be assigned more than a single functional RNA structure throughout their cellular life and that structure formation happens co-transcriptionally, i.e. as the transcript is synthesised in the cell. Moreover, functional RNA structures are not limited to non-coding transcripts, but can also feature in coding transcripts. The picture that now emerges is that RNA structures constitute an additional layer of information that can be encoded in any RNA transcript (and on top of other layers of information such as protein-context) in order to exert a wide range of functional roles. Moreover, different encoded RNA structures can be expressed at different stages of a transcript's life in order to alter the transcript's behaviour depending on its actual cellular context. Similar to the concept of alternative splicing for protein-coding genes, where a single transcript can yield different proteins depending on cellular context, it is thus appropriate to propose the notion of alternative RNA structure expression for any given transcript. This review introduces several computational strategies that my group developed to detect different aspects of RNA structure expression in vivo. Two aspects are of particular interest to us: (1) RNA secondary structure features that emerge during co-transcriptional folding and (2) functional RNA structure features that are expressed at different times of a transcript's life and potentially mutually exclusive.

A sensitivity analysis of RNA folding nearest neighbor parameters identifies a subset of free energy parameters with the greatest impact on RNA secondary structure prediction.

Nearest neighbor parameters for estimating the folding energy changes of RNA secondary structures are used in structure prediction and analysis. Despite their widespread application, a comprehensive analysis of the impact of each parameter on the precision of calculations had not been conducted. To identify the parameters with greatest impact, a sensitivity analysis was performed on the 291 parameters that compose the 2004 version of the free energy nearest neighbor rules. Perturbed parameter sets were generated by perturbing each parameter independently. Then the effect of each individual parameter change on predicted base-pair probabilities and secondary structures as compared to the standard parameter set was observed for a set of sequences including structured ncRNA, mRNA and randomized sequences. The results identify for the first time the parameters with the greatest impact on secondary structure prediction, and the subset which should be prioritized for further study in order to improve the precision of structure prediction. In particular, bulge loop initiation, multibranch loop initiation, AU/GU internal loop closure and AU/GU helix end parameters were particularly important. An analysis of parameter usage during folding free energy calculations of stochastic samples of secondary structures revealed a correlation between parameter usage and impact on structure prediction precision.

Defects in RNA metabolism in mitochondrial disease

The expression of mitochondrially-encoded genes requires the efficient processing of long precursor RNAs at the 5′ and 3′ ends of tRNAs, a process which, when disrupted, results in disease. Two such mutations reside within mt-tRNALeu(UUR); a m.3243A>G transition, which is the most common cause of MELAS (mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes), and m.3302A>G which often causes mitochondrial myopathy (MM). We used parallel analysis of RNA ends (PARE) that captures the 5′ terminal end of 5′-monophosphorylated mitochondrial RNAs to compare the effects of the m.3243A>G and m.3302A>G mutations on mitochondrial tRNA processing and downstream RNA metabolism. We confirmed previously identified RNA processing defects, identified common internal cleavage sites and new sites unique to the m.3243A>G mutants that do not correspond to transcript ends. These sites occur in regions of predicted RNA secondary structure, or are in close proximity to such regions, and may identify regions of importance to the processing of mtRNAs.

Associating genetic variation at Perilipin 1, Complement Factor D and Adiponectin loci to the bone health status in North Indian population

Osteoporosis, the most common bone metabolic disease affecting nearly 200 million people worldwide is under the strong influence of genetic components. Simultaneously, adipogenesis and osteogenesis are two highly coordinated processes imperative for the maintenance of bone quality and quantity, where any perturbation leads to pathological conditions of obesity, osteopenia and osteoporosis. To delineate this adipogenic-osteogenic connection, a total of 254 cases (T-score<−1.0 SD) and 250 age, gender and ethnicity matched healthy controls (T-score≥−1.0 SD) were recruited from North India after analyzing bone health status employing quantitative ultrasound (QUS) bone densitometer. The genetic variants of Perilipin 1 (PLIN1), Complement Factor D (CFD) and Adiponectin (ADIPOQ) were genotyped using the PCR-RFLP/ARMS-PCR approach. Subjects with CC+CT (PLIN1 rs2304795) and CC+CG (CFD rs1683563) genotypes conferred nearly 1.54–1.87 fold increased risk towards bone deterioration. Predicted RNA secondary structures of rs2304795 corroborated the risk associated with wild type C allele. G allele carriers at the ADIPOQ locus (rs1501299) were more likely to have a lower bone health (1.57-fold). Haplotype analysis revealed the ADIPOQ variants rs1501299 and rs3774261 in slight linkage disequilibrium (LD), nonetheless G/G haplotype was associated with increased risk. 3-locus and 5-locus gene-gene interaction models revealed a greater likelihood of bone deterioration. In conclusion, certain variants of adipogenic genes might serve as potential biomarkers for determining the genetic predisposition towards bone loss in the North Indian population, further, emphasizing the role of impaired metabolism in bone health.

Blind Predictions of RNA/Protein Relative Binding Affinities

Interactions between RNA and proteins are pervasive in biology, shaping processes such as mRNA translation, localization, and alternative splicing. Developing a predictive understanding of the energetics of these systems would allow us to model biologically relevant mutations of these interactions and ultimately design novel interactions. Despite recent advances in high throughput experimental technologies that measure the energetics of these systems, quantitative computational prediction of relative RNA/protein binding affinities has remained a challenge. This is partly due to the observation that computational binding affinity prediction methods typically break down when the molecules are highly flexible or undergo significant conformational changes, situations that often arise in RNA/protein binding. Here, we present a novel framework within Rosetta for predicting RNA/protein relative binding affinities that begins to address this issue. Specifically, we show that the nearest neighbor energies, which are typically used for RNA secondary structure prediction, can be used to approximate the unbound free energy of the RNA, thus eliminating the need to explicitly account for the flexibility of the unbound RNA or conformational changes of the RNA upon binding. Using this method of calculating the unbound RNA free energy significantly improves the prediction accuracy over a more typical 3D structure-based approach. We optimized this method using a subset of published MS2 coat protein affinities and ultimately made predictions for the system with 1.11-1.28 kcal/mol root mean square (RMS) error. Additionally, we show that this method is able to predict relative binding affinities for four diverse RNA/protein systems with 1.48 kcal/mol RMS error. Finally, to more rigorously assess this method, we independently measured and made blind predictions for PUF3 and PUM2 binding affinities with RMS errors of 1-2 kcal/mol, which is comparable to the accuracy achieved by prediction methods for other types of systems.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5′-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson–Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Novel poly-uridine insertion in the 3′UTR and E2 amino acid substitutions in a low virulent classical swine fever virus

In this study, we compared the virulence in weaner pigs of the Pinar del Rio isolate and the virulent Margarita strain. The latter caused the Cuban classical swine fever (CSF) outbreak of 1993. Our results showed that the Pinar del Rio virus isolated during an endemic phase is clearly of low virulence. We analysed the complete nucleotide sequence of the Pinar del Rio virus isolated after persistence in newborn piglets, as well as the genome sequence of the inoculum. The consensus genome sequence of the Pinar del Rio virus remained completely unchanged after 28days of persistent infection in swine. More importantly, a unique poly-uridine tract was discovered in the 3′UTR of the Pinar del Rio virus, which was not found in the Margarita virus or any other known CSFV sequences. Based on RNA secondary structure prediction, the poly-uridine tract results in a long single-stranded intervening sequence (SS) between the stem-loops I and II of the 3′UTR, without major changes in the stem- loop structures when compared to the Margarita virus. The possible implications of this novel insertion on persistence and attenuation remain to be investigated. In addition, comparison of the amino acid sequence of the viral proteins Erns, E1, E2 and p7 of the Margarita and Pinar del Rio viruses showed that all non-conservative amino acid substitutions acquired by the Pinar del Rio isolate clustered in E2, with two of them being located within the B/C domain. Immunisation and cross-neutralisation experiments in pigs and rabbits suggest differences between these two viruses, which may be attributable to the amino acid differences observed in E2. Altogether, these data provide fresh insights into viral molecular features which might be associated with the attenuation and adaptation of CSFV for persistence in the field.

Thermodynamic stabilities of three-way junction nanomotifs in prohead RNA.

The thermodynamic stabilities of four natural prohead or packaging RNA (pRNA) three-way junction (3WJ) nanomotifs and seven phi29 pRNA 3WJ deletion mutant nanomotifs were investigated using UV optical melting on a three-component RNA system. Our data reveal that some pRNA 3WJs are more stable than the phi29 pRNA 3WJ. The stability of the 3WJ contributes to the unique self-assembly properties of pRNA. Thus, ultrastable pRNA 3WJ motifs suggest new scaffolds for pRNA-based nanotechnology. We present data demonstrating that pRNA 3WJs differentially respond to the presence of metal ions. A comparison of our data with free energies predicted by currently available RNA secondary structure prediction programs shows that these programs do not accurately predict multibranch loop stabilities. These results will expand the existing parameters used for RNA secondary structure prediction from sequence in order to better inform RNA structure–function hypotheses and guide the rational design of functional RNA supramolecular assemblies.

In Silico Prediction of RNA Secondary Structure.

The secondary structure of an RNA molecule represents the base-pairing interactions within the molecule and fundamentally determines its overall structure. In this chapter, we overview the main approaches and existing tools for predicting RNA secondary structures, as well as methods for identifying noncoding RNAs from genomic sequences or RNA sequencing data. We then focus on the identification of a well-known class of small noncoding RNAs, namely microRNAs, which play very important roles in many biological processes through regulating post-transcriptionally the expression of genes and which dysregulation has been shown to be involved in several human diseases.

Mutational analysis of hepatitis E virus ORF1 "Y-domain": Effects on RNA replication and virion infectivity.

AIMTo investigate the role of non-structural open reading frame 1 “Y-domain” sequences in the hepatitis E virus (HEV) life cycle.METHODSSequences of human HEV Y-domain (amino acid sequences 216-442) and closely-related viruses were analyzed in silico. Site-directed mutagenesis of the Y-domain (HEV SAR55) was carried out and studied in the replicon-baculovirus-hepatoma cell model. In vitro transcribed mRNA (pSK-GFP) constructs were transfected into S10-3 cells and viral RNA replicating GFP-positive cells were scored by flow cytometry. Mutant virions’ infectivity was assayed on naïve HepG2/C3A cells.RESULTSIn silico analysis identified a potential palmitoylation-site (C336C337) and an α-helix segment (L410Y411S412W413L414F415E416) in the HEV Y-domain. Molecular characterization of C336A, C337A and W413A mutants of the three universally conserved residues showed non-viability. Further, of the 10 consecutive saturation mutants covering the entire Y-domain nucleotide sequences (nts 650-1339), three constructs (nts 788-994) severely affected virus replication. This revealed the indispensability of the internal sequences but not of the up- or downstream sequences at the transcriptional level. Interestingly, the three mutated residues corresponded to the downstream codons that tolerated saturation mutation, indicating their post-translational functional/structural essentiality. In addition, RNA secondary structure prediction revealed formation of stable hairpins (nts 788-994) where saturation mutation drastically inhibited virion infectivity.CONCLUSIONThis is the first demonstration of the critical role of Y-domain sequences in HEV life cycle, which may involve gene regulation and/or membrane binding in intracellular replication complexes.