The secondary structure of human 28S rRNA: The structure and evolution of a mosaic rRNA gene

Summary We have determined the secondary structure of the human 28S rRNA molecule based on comparative analysis of available eukaryotic cytoplasmic and prokaryotic large-rRNA gene sequences. Examination of large-rRNA sequences of both distantly and closely related species has enabled us to derive a structure that accounts both for highly conserved sequence tracts and for previously unanalyzed variable-sequence tracts that account for the evolutionary differences in size among the large rRNAs.Human 28S rRNA is composed of two different types of sequence tracts: conserved and variable. They differ in composition, degree of conservation, and evolution. The conserved regions demonstrate a striking constancy of size and sequence. We have confirmed that the conserved regions of large-rRNA molecules are capable of forming structures that are superimposable on one another. The variable regions contain the sequences responsible for the 83% increase in size of the human large-rRNA molecule over that ofEscherichia coli. Their locations in the gene are maintained during evolution. They are G+C rich and largely nonhomologous, contain simple repetitive sequences, appear to evolve by frequent recombinational events, and are capable of forming large, stable hairpins.The secondary-structure model presented here is in close agreement with existing prokaryotic 23S rRNA secondary-structure models. The introduction of this model helps resolve differences between previously proposed prokaryotic and eukaryotic large-rRNA secondary-structure models.     

Nuclease S1 analysis of eubacterial 5S rRNA secondary structure

Summary Single-strand-specific nuclease S₁ was employed as a structural probe to confirm locations of unpaired nucleotide bases in 5S rRNAs purified from prokaryotic species of rRNA superfamily I. Limited nuclease S₁ digests of 3- and 5-end-labeled [³²P]5S rRNAs were electrophoresed in parallel with reference endoribonuclease digests on thin allel with reference endoribonuclease digests on thin sequencing gels. Nuclease S₁ primary hydrolysis patterns were comparable for 5S rRNAs prepared from all 11 species examined in this study. The locations of base-paired regions determined by enzymatic analysis corroborate the general features of the proposed universal five-helix model for prokaryotic 5S rRNA, although the results of this study suggest a significant difference between prokaryotic and eukaryotic 5S rRNAs in the evolution of helix IV. Furthermore, the extent of base-pairing predicted by helix IV needs to be reevaluated for eubacterial species. Clipping patterns in helices II and IV appear to be consistent with a secondary structural model that undergoes a conformational rearrangement between two (or more) structures. Primary clipping patterns in the helix II region, obtained by S₁ analysis, may provide useful information concerning the tertiary structure of the 5S rRNA molecule.     

General combinatorics of RNA secondary structure

The total number of RNA secondary structures of a given length with minimal hairpin loop length m(m>0) and with minimal stack length l(l>0) is computed, under the assumption that all base pairs can occur. Asymptotics are derived from the determination of recurrence relations of decomposition properties.     

18S rRNA alignments derived from different secondary structure models can produce alternative phylogenies

Molecular sequence data are often aligned on the basis of secondary and/or tertiary structure models. However, these models are regularly updated and sometimes differ depending on the way in which they were constructed. We examined whether the choice of a particular 18S rRNA secondary structure model as alignment basis influences phylogeny inference. We therefore compared 18S rRNA phylogenies derived from alignments based on different models. We used: 1. Maximum parsimony; 2. The neighbour-joining method; 3. The maximum-likelihood approach; and 4. Evolutionary parsimony. This demonstrated that the secondary structure model on which an alignment is based may influence: 1. The tree topologies found by these four methods; 2. The numbers of most parsimonious trees found; and 3. The statistical values calculated by the evolutionary parsimony method.     

Mitochondrial 12S rRNA gene mutations affect RNA secondary structure and lead to variable penetrance in hearing impairment

Mutations in the mitochondrial DNA are one of the most important causes of sensorineural hearing loss, especially in the 12S ribosomal RNA (rRNA) gene. We have analyzed the mtDNA 12S rRNA gene in a cohort of 443 families with hearing impairment, and have identified the A1555G mutation in 69 unrelated cases. A1555G is not a fully penetrant change, since only 63% of subjects with this change have developed hearing impairment. In addition, only 22% of the 183 A1555G deaf subjects were treated with aminoglycosides. Two novel nucleotide changes (T1291C and T1243C) were identified. T1243C was found in five deafness cases and one control sample. Mutation T1291C was detected in all maternally related individuals of a pedigree and in none of 95 control samples. Conservation analysis and comparison of the 12S rRNA structure with the 16S rRNA of Escherichia coli showed that the T at nucleotide 1243 and A at nucleotide 1555 are conserved positions. Prediction of RNA secondary structure showed changes in all 12S rRNA variants, the most severe being for T1291C. The reported data confirm the high prevalence of mutation A1555G in deafness cases and the major role of the 12S rRNA gene in hearing. The two novel changes reported here might have different contributions as deafness-related variants. T1291C fulfills the criteria of a disease-causing change. As in the case of mutation A1555G, the underlying phenotype of T1291C is not homogeneous for all family members, providing evidence for the implication of environmental and/or additional genetic factors.     

Prediction of secondary structures of 16S and 23S rRNA fromEscherichia coli

Small and large subunits ofEscherichia coli ribosome have three different rRNAs, the sequences of which are known. However, attempts by three groups to predict secondary structures of 16S and 23S rRNAs have certain common limitations namely, these structures are predicted assuming no interactions among various domains of the molecule and only 40% residues are involved in base pairing as against the experimental observation of 60 % residues in base paired state. Recent experimental studies have shown that there is a specific interaction between naked 16S and 23S rRNA molecules. This is significant because we have observed that the regions (oligonucleotides of length 9–10 residues), in 16S rRNA which are complementary to those in 23S rRNA do not have internal complementary sequences. Therefore, we have developed a simple graph theoretical approach to predict secondary structures of 16S and 23S rRNAs. Our method for model building not only uses complete sequence of 16S or 23S rRNA molecule along with other experimental observations but also takes into account the observation that specific recognition is possible through the complementary sequences between 16S and 23S rRNA molecules and, therefore, these parts of the molecules are not used for internal base pairing. The method used to predict secondary structures is discussed. A typical secondary structure of the complex between 16S and 23S rRNA molecules, obtained using our method, is presented and compared Briefly with earlier model Building studies.     

Measuring the thermodynamics of RNA secondary structure formation

The thermodynamics of RNA secondary structure formation in small model systems provides a database for predicting RNA structure from sequence. Methods for making these measurements are reviewed with emphasis on optical methods and treatment of experimental errors. Analysis of experimental results in terms of simple nearest-neighbor models is presented. Some measured sequence dependences of non-Watson-Crick motifs are discussed. © 1998 John Wiley & Sons, Inc. Biopoly 44: 309–319, 1997     

Improved free energy parameters for RNA pseudoknotted secondary structure prediction

Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks–Pierce (DP) and the Cao–Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.     

Dynalign: an algorithm for finding the secondary structure common to two RNA sequences

With the rapid increase in the size of the genome sequence database, computational analysis of RNA will become increasingly important in revealing structure-function relationships and potential drug targets. RNA secondary structure prediction for a single sequence is 73 % accurate on average for a large database of known secondary structures. This level of accuracy provides a good starting point for determining a secondary structure either by comparative sequence analysis or by the interpretation of experimental studies. Dynalign is a new computer algorithm that improves the accuracy of structure prediction by combining free energy minimization and comparative sequence analysis to find a low free energy structure common to two sequences without requiring any sequence identity. It uses a dynamic programming construct suggested by Sankoff. Dynalign, however, restricts the maximum distance, M, allowed between aligned nucleotides in the two sequences. This makes the calculation tractable because the complexity is simplified to O(M(3)N(3)), where N is the length of the shorter sequence.The accuracy of Dynalign was tested with sets of 13 tRNAs, seven 5 S rRNAs, and two R2 3' UTR sequences. On average, Dynalign predicted 86.1 % of known base-pairs in the tRNAs, as compared to 59.7 % for free energy minimization alone. For the 5 S rRNAs, the average accuracy improves from 47.8 % to 86.4 %. The secondary structure of the R2 3' UTR from Drosophila takahashii is poorly predicted by standard free energy minimization. With Dynalign, however, the structure predicted in tandem with the sequence from Drosophila melanogaster nearly matches the structure determined by comparative sequence analysis.     

Sequence and secondary structure of the central domain ofDrosophila 26S rRNA: A universal model for the central domain of the large rRNA containing the region in which the central break may happen

Summary An 890-bp sequence from the central region ofDrosophila melanogaster 26S ribosomal DNA (rDNA) has been determined and used in an extensive comparative analysis of the central domain of the large subunit ribosomal RNA (lrRNA) from prokaryotes, organelles, and eukaryotes. An alignment of these different sequences has allowed us to precisely map the regions of the central domain that have highly diverged during evolution. Using this sequence comparison, we have derived a secondary structure model of the central domain ofDrosophila 26S ribosomal RNA (rRNA). We show that a large part of this model can be applied to the central domain of lrRNA from prokaryotes, eukaryotes, and organelles, therefore defining a universal common structural core. Likewise, a comparative study of the secondary structure of the divergent regions has been performed in several organisms. The results show that, despite a nearly complete divergence in their length and sequence, a common structural core is also present in divergent regions. In some organisms, one or two of the divergent regions of the central domain are removed by processing events. The sequence and structure of these regions (fragmentation spacers) have been compared to those of the corresponding divergent regions that remain part of the mature rRNA in other species.     

RNA secondary structure and in vitro translation efficiency

Cell-free protein synthesis is a promising technology featuring many advantages compared to in vivo expression techniques. However, most proteins are still synthesized in vivo due to relatively low protein yields commonly achieved in vitro, especially in the batch mode of reaction. In Escherichia coli S30 extract-based cell-free systems protein yields are supposed to be partially limited by a secondary structure formation of the mRNA. In this study we checked promising members of various classes of RNA chaperones and several different RNA helicases on their ability to enhance in vitro translation. The data clearly show that the addition of none of these factors provides a general solution to the problem. However, protein yields can be increased in presence of a microRNA hybridizing with the 5′ untranslated region of mRNAs, possibly by inducing structural changes improving accessibility of the Shine Dalgarno sequence for the ribosomes.     

Secondary structure model for the ITS-2 precursor rRNA of strongyloid nematodes of equids: implications for phylogenetic inference

In order to maximise the positional homology in the primary sequence alignment of the second internal transcribed spacer for 30 species of equine strongyloid nematodes, the secondary structures of the precursor ribosomal RNA were predicted using an approach combining an energy minimisation method and comparative sequence analysis. The results indicated that a common secondary structure model of the second internal transcribed spacer of these nematodes was maintained, despite significant interspecific differences (2–56%) in primary sequences. The secondary structure model was then used to refine the primary second internal transcribed spacer sequence alignment. The “manual” and “structure” alignments were both subjected to phylogenetic analysis using three different tree-building methods to compare the effect of using different sequence alignments on phylogenetic inference. The topologies of the phylogenetic trees inferred from the manual second internal transcribed spacer alignment were usually different to those derived from the structure second internal transcribed spacer alignment. The results suggested that the positional homology in the second internal transcribed spacer primary sequence alignment was maximised when the secondary structure model was taken into consideration.     

Semiautomated improvement of RNA alignments

We have developed a semiautomated RNA sequence editor (SARSE) that integrates tools for analyzing RNA alignments. The editor highlights different properties of the alignment by color, and its integrated analysis tools prevent the introduction of errors when doing alignment editing. SARSE readily connects to external tools to provide a flexible semiautomatic editing environment. A new method, Pcluster, is introduced for dividing the sequences of an RNA alignment into subgroups with secondary structure differences. Pcluster was used to evaluate 574 seed alignments obtained from the Rfam database and we identified 71 alignments with significant prediction of inconsistent base pairs and 102 alignments with significant prediction of novel base pairs. Four RNA families were used to illustrate how SARSE can be used to manually or automatically correct the inconsistent base pairs detected by Pcluster: the mir-399 RNA, vertebrate telomase RNA (vert-TR), bacterial transfer-messenger RNA (tmRNA), and the signal recognition particle (SRP) RNA. The general use of the method is illustrated by the ability to accommodate pseudoknots and handle even large and divergent RNA families. The open architecture of the SARSE editor makes it a flexible tool to improve all RNA alignments with relatively little human intervention. Online documentation and software are available at (http://sarse.ku.dk).     

Revolutions in RNA secondary structure prediction

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.     

Visualizing the global secondary structure of a viral RNA genome with cryo-electron microscopy

The lifecycle, and therefore the virulence, of single-stranded (ss)-RNA viruses is regulated not only by their particular protein gene products, but also by the secondary and tertiary structure of their genomes. The secondary structure of the entire genomic RNA of satellite tobacco mosaic virus (STMV) was recently determined by selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE). The SHAPE analysis suggested a single highly extended secondary structure with much less branching than occurs in the ensemble of structures predicted by purely thermodynamic algorithms. Here we examine the solution-equilibrated STMV genome by direct visualization with cryo-electron microscopy (cryo-EM), using an RNA of similar length transcribed from the yeast genome as a control. The cryo-EM data reveal an ensemble of branching patterns that are collectively consistent with the SHAPE-derived secondary structure model. Thus, our results both elucidate the statistical nature of the secondary structure of large ss-RNAs and give visual support for modern RNA structure determination methods. Additionally, this work introduces cryo-EM as a means to distinguish between competing secondary structure models if the models differ significantly in terms of the number and/or length of branches. Furthermore, with the latest advances in cryo-EM technology, we suggest the possibility of developing methods that incorporate restraints from cryo-EM into the next generation of algorithms for the determination of RNA secondary and tertiary structures.     

Incorporating global features of RNA motifs in predictions for an ensemble of secondary structures for encapsidated MS2 bacteriophage RNA

The secondary structure of encapsidated MS2 genomic RNA poses an interesting RNA folding challenge. Cryoelectron microscopy has demonstrated that encapsidated MS2 RNA is well-ordered. Models of MS2 assembly suggest that the RNA hairpin-protein interactions and the appropriate placement of hairpins in the MS2 RNA secondary structure can guide the formation of the correct icosahedral particle. The RNA hairpin motif that is recognized by the MS2 capsid protein dimers, however, is energetically unfavorable, and thus free energy predictions are biased against this motif. Computer programs called Crumple, Sliding Windows, and Assembly provide useful tools for prediction of viral RNA secondary structures when the traditional assumptions of RNA structure prediction by free energy minimization may not apply. These methods allow incorporation of global features of the RNA fold and motifs that are difficult to include directly in minimum free energy predictions. For example, with MS2 RNA the experimental data from SELEX experiments, crystallography, and theoretical calculations of the path for the series of hairpins can be incorporated in the RNA structure prediction, and thus the influence of free energy considerations can be modulated. This approach thoroughly explores conformational space and generates an ensemble of secondary structures. The predictions from this new approach can test hypotheses and models of viral assembly and guide construction of complete three-dimensional models of virus particles.     

Phylogeny of the 5S ribosomal RNA fromSynechococcus lividus II: The cyanobacterial/chloroplast 5S RNAs form a common structural class

Summary The complete nucleotide sequence of the 5S ribosomal RNA from the cyanobacteriumSynechococcus lividus II has been determined. The sequence is 5-UGCCUAGUGUUUAUGGCGCG-GUGGAACCACGCUGAUCCAUCCCGAACUC-AGAGGUGAAACAUCGCAGCGGUGAAGAU-AGUUGGAGGGUAGCCUCCUGCAAAAAUA-GCUCAAUGCUAGGCA_OH-3. This 5S RNA has the cyanobacterial- and chloroplast-specific nucleotide insertion between positions 30 and 31 (using the numbering system of the generalized eubacterial 5S RNA) and the chloroplast-specific nucleotide-deletion signature between positions 34 and 39. The 5S RNA ofS. lividus II has 27 base differences compared with the 5S RNA of the related strainS. lividus III. This large difference may reflect an ancient divergence between these two organisms. The electrophoretic mobilities on nondenaturing polyacrylamide gels of renatured 5S RNAs fromS. lividus II,S. lividus III, and spinach chloroplasts are identical, but differ considerably from that ofEscherichia coli 5S RNA. This most likely reflects differences in higher-order structure between the 5S RNA ofE. coli and these cyanobacterial and chloroplast 5S RNAs.     

A new algorithm for RNA secondary structure design

The function of many RNAs depends crucially on their structure. Therefore, the design of RNA molecules with specific structural properties has many potential applications, e.g. in the context of investigating the function of biological RNAs, of creating new ribozymes, or of designing artificial RNA nanostructures. Here, we present a new algorithm for solving the following RNA secondary structure design problem: given a secondary structure, find an RNA sequence (if any) that is predicted to fold to that structure. Unlike the (pseudoknot-free) secondary structure prediction problem, this problem appears to be hard computationally. Our new algorithm, "RNA Secondary Structure Designer (RNA-SSD)", is based on stochastic local search, a prominent general approach for solving hard combinatorial problems. A thorough empirical evaluation on computationally predicted structures of biological sequences and artificially generated RNA structures as well as on empirically modelled structures from the biological literature shows that RNA-SSD substantially out-performs the best known algorithm for this problem, RNAinverse from the Vienna RNA Package. In particular, the new algorithm is able to solve structures, consistently, for which RNAinverse is unable to find solutions. The RNA-SSD software is publically available under the name of RNA Designer at the RNASoft website (www.rnasoft.ca).     

ProbKnot: Fast prediction of RNA secondary structure including pseudoknots

It is a significant challenge to predict RNA secondary structures including pseudoknots. Here, a new algorithm capable of predicting pseudoknots of any topology, ProbKnot, is reported. ProbKnot assembles maximum expected accuracy structures from computed base-pairing probabilities in O(N²) time, where N is the length of the sequence. The performance of ProbKnot was measured by comparing predicted structures with known structures for a large database of RNA sequences with fewer than 700 nucleotides. The percentage of known pairs correctly predicted was 69.3%. Additionally, the percentage of predicted pairs in the known structure was 61.3%. This performance is the highest of four tested algorithms that are capable of pseudoknot prediction. The program is available for download at: http://rna.urmc.rochester.edu/RNAstructure.html.