18S rRNA secondary structure and phylogenetic position of Peloridiidae (Insecta, hemiptera)

A secondary structure model for 18S rRNA of peloridiids, relict insects with a present-day circumantarctic distribution, is constructed using comparative sequence analysis, thermodynamic folding, a consensus method using 18S rRNA models of other taxa, and support of helices based on compensatory substitutions. Results show that probable in vivo configuration of 18S rRNA is not predictable using current free-energy models to fold the entire molecule concurrently. This suggests that refinements in free-energy minimization algorithms are needed. Molecular phylogenetic datasets were created using 18S rRNA nucleotide alignments produced by CLUSTAL and rigorous interpretation of homologous position based on certain secondary substructures. Phylogenetic analysis of a hemipteran data matrix of 18S rDNA sequences placed peloridiids sister to Heteroptera. Resolution of affiliations between the three main euhemipteran lineages was unresolved. The peloridiid 18S RNA model presented here provides the most accurate template to date for aligning homologous nucleotides of hemipteran taxa. Using folded 18S rRNA to infer homology of character as morpho-molecular structures or nucleotides and scoring particular sites or substructures is discussed.     

Structure analysis of the 5' external transcribed spacer of the precursor ribosomal RNA from Saccharomyces cerevisiae.

Full-length precursor ribosomal RNA molecules were produced in vitro using as a template, a plasmid containing the yeast 35 S pre-rRNA gene under the control of the phage T3 promoter. The higher-order structure of the 5'-external transcribed spacer (5' ETS) sequence in the 35S pre-rRNA molecule was studied using dimethylsulfate, 1-cyclohexyl-3-(2-morpholinoethyl)-carbodiimide metho-p-toluenesulfonate, RNase T1 and RNase V1 as structure-sensitive probes. Modified residues were detected by primer extension. Data produced were used to evaluate several theoretical structure models predicted by minimum free-energy calculations. A model for the entire 5'ETS region is proposed that accommodates 82% of the residues experimentally shown to be in either base-paired or single-stranded structure in the correct configuration. The model contains a high degree of secondary structure with ten stable hairpins of varying lengths and stabilities. The hairpins are composed of the Watson-Crick A.T and G.C pairs plus the non-canonical G.U pairs. Based on a comparative analysis of the 5' ETS sequence from Saccharomyces cerevisiae and Schizosaccharomyces pombe, most of the base-paired regions in the proposed model appear to be phylogenetically supported. The two sites previously shown to be crosslinked to U3 snRNA as well as the previously proposed recognition site for processing and one of the early processing site (based on sequence homology to the vertebrate ETS cleavage site) are located in single-stranded regions in the model. The present folding model for the 5' ETS in the 35 S pre-rRNA molecule should be useful in the investigations of the structure, function and processing of pre-rRNA.     

Structure clustering features on the Sfold Web server

SUMMARY: The energy landscape of RNA secondary structures is often complex, and the Boltzmann-weighted ensemble usually contains distinct clusters. Furthermore, the minimum free energy structure often lies outside of the cluster containing the structure determined by comparative sequence analysis. We have developed procedures to characterize and visualize the Boltzmann-weighted ensemble, and have made them available on the Sfold Web server. The new features on the Web server include clustering statistics, ensemble and cluster centroids, multi-dimensional scaling display and energy landscape representation of the Boltzmann-weighted ensemble. AVAILABILITY: http://sfold.wadsworth.org; http://www.bioinfo.rpi.edu/applications/sfold CONTACT: chanc@wadsworth.org.     

Evaluating tilt angles of membrane-associated helices: comparison of computational and NMR techniques

A computational method to calculate the orientation of membrane-associated alpha-helices with respect to a lipid bilayer has been developed. It is based on a previously derived implicit membrane representation, which was parameterized using the structures of 46 alpha-helical membrane proteins. The method is validated by comparison with an independent data set of six transmembrane and nine antimicrobial peptides of known structure and orientation. The minimum energy orientations of the transmembrane helices were found to be in good agreement with tilt and rotation angles known from solid-state NMR experiments. Analysis of the free-energy landscape found two types of minima for transmembrane peptides: i), Surface-bound configurations with the helix long axis parallel to the membrane, and ii), inserted configurations with the helix spanning the membrane in a perpendicular orientation. In all cases the inserted configuration also contained the global energy minimum. Repeating the calculations with a set of solution NMR structures showed that the membrane model correctly distinguishes native transmembrane from nonnative conformers. All antimicrobial peptides investigated were found to orient parallel and bind to the membrane surface, in agreement with experimental data. In all cases insertion into the membrane entailed a significant free-energy penalty. An analysis of the contributions of the individual residue types confirmed that hydrophobic residues are the main driving force behind membrane protein insertion, whereas polar, charged, and aromatic residues were found to be important for the correct orientation of the helix inside the membrane.     

A search for H/ACA snoRNAs in yeast using MFE secondary structure prediction

MOTIVATION: Noncoding RNA genes produce functional RNA molecules rather than coding for proteins. One such family is the H/ACA snoRNAs. Unlike the related C/D snoRNAs these have resisted automated detection to date. RESULTS: We develop an algorithm to screen the yeast genome for novel H/ACA snoRNAs. To achieve this, we introduce some new methods for facilitating the search for noncoding RNAs in genomic sequences which are based on properties of predicted minimum free-energy (MFE) secondary structures. The algorithm has been implemented and can be generalized to enable screening of other eukaryote genomes. We find that use of primary sequence alone is insufficient for identifying novel H/ACA snoRNAs. Only the use of secondary structure filters reduces the number of candidates to a manageable size. From genomic context, we identify three strong H/ACA snoRNA candidates. These together with a further 47 candidates obtained by our analysis are being experimentally screened.     

Interpreting oligonucleotide microarray data to determine RNA secondary structure: application to the 3' end of Bombyx mori R2 RNA

A method to deduce RNA secondary structure on the basis of data from microarrays of 2'-O-methyl RNA 9-mers immobilized in agarose film on glass slides is tested with a 249 nucleotide RNA from the 3' end of the R2 retrotransposon from Bombyx mori. Various algorithms incorporating binding data and free-energy minimization calculations were compared for interpreting the data to provide possible secondary structures. Two different methods give structures with 100 and 87% of the base pairs determined by sequence comparison. In contrast, structures predicted by free-energy minimization alone by Mfold and RNAstructure contain 52 and 72% of the known base pairs, respectively. This combination of high throughput microarray techniques with algorithms using free-energy calculations has potential to allow for fast determination of RNA secondary structure. It should also facilitate the design of antisense and siRNA oligonucleotides.     

Computational approaches for RNA energy parameter estimation

Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.     

Simulating RNA folding kinetics on approximated energy landscapes

We present a general computational approach to simulate RNA folding kinetics that can be used to extract population kinetics, folding rates and the formation of particular substructures that might be intermediates in the folding process. Simulating RNA folding kinetics can provide unique insight into RNA whose functions are dictated by folding kinetics and not always by nucleotide sequence or the structure of the lowest free-energy state. The method first builds an approximate map (or model) of the folding energy landscape from which the population kinetics are analyzed by solving the master equation on the map. We present results obtained using an analysis technique, map-based Monte Carlo simulation, which stochastically extracts folding pathways from the map. Our method compares favorably with other computational methods that begin with a comprehensive free-energy landscape, illustrating that the smaller, approximate map captures the major features of the complete energy landscape. As a result, our method scales to larger RNAs. For example, here we validate kinetics of RNA of more than 200 nucleotides. Our method accurately computes the kinetics-based functional rates of wild-type and mutant ColE1 RNAII and MS2 phage RNAs showing excellent agreement with experiment.     

Energy landscape of k-point mutants of an RNA molecule

MOTIVATION: A k-point mutant of a given RNA sequence s = s(1), ..., s(n) is an RNA sequence s' = s'(1),..., s'(n) obtained by mutating exactly k-positions in s; i.e. Hamming distance between s and s' equals k. To understand the effect of pointwise mutation in RNA, we consider the distribution of energies of all secondary structures of k-point mutants of a given RNA sequence. RESULTS: Here we describe a novel algorithm to compute the mean and standard deviation of energies of all secondary structures of k-point mutants of a given RNA sequence. We then focus on the tail of the energy distribution and compute, using the algorithm AMSAG, the k-superoptimal structure; i.e. the secondary structure of a < or =k-point mutant having least free energy over all secondary structures of all k'-point mutants of a given RNA sequence, for k' < or = k. Evidence is presented that the k-superoptimal secondary structure is often closer, as measured by base pair distance and two additional distance measures, to the secondary structure derived by comparative sequence analysis than that derived by the Zuker minimum free energy structure of the original (wild type or unmutated) RNA.     

Structural analysis of the internal transcribed spacer 2 of the precursor ribosomal RNA from Saccharomyces cerevisiae

Full-length precursor ribosomal RNA molecules (6440 bases) were produced in vitro using a plasmid containing the yeast 35 S pre-rRNA operon under the control of phage T7 promoter. The higher-order structure of the internal transcribed spacer 2 (ITS-2) region (between the 5.8 S and 25 S rRNA sequence) in the pre-rRNA molecule was investigated using a combination of enzymatic and chemical structural probes. The data were used to evaluate several structural models predicted by a minimum free-energy calculation. The results supported a model in which the 3' end of the 5.8 S rRNA and the 5' end of the 25 S rRNA are hydrogen-bonded better than the one in which the ends are not. The model contains a high degree of secondary structure with several stable hairpins. Similar structural models for the ITS-2 regions of Schizosaccharomyces pombe, Saccharomyces carlsbergensis, mung bean and Xenopus laevis were derived. Certain common folding features appear to be conserved, in spite of extensive sequence divergence. The yeast model should be useful as a prototype in future investigations of the structure, function and processing of pre-rRNA.     

The role of mRNA structure in the regulation of protein synthesis: Implications for studies of development

A method is described for the experimental determination of the secondary structure of RNA using enzymatic cleavage data coupled with computer analysis. The structure-specific enzymes S1 nuclease and cobra venom ribonuclease are used to locate nonpaired and basepaired nucleotides, respectively. Computer techniques that utilize the enzymatic susceptibility information to generate a minimum free-energy structure are used to obtain secondary structure models. A second method, using acrylamide-agarose gel electrophoresis, is described for the determination of the relative protein synthesis initiation rates of endlabeled eukaryotic mRNAs. These methods are applied to the rabbit globin mRNAs as an example of a general approach for relating mRNA structure and function. A discussion of the role of messenger RNA structure in the regulation of translation is included with an emphasis on studies of development.     

RNA secondary structure prediction based on free energy and phylogenetic analysis.

We describe a computational method for the prediction of RNA secondary structure that uses a combination of free energy and comparative sequence analysis strategies. Using a homology-based sequence alignment as a starting point, all favorable pairings with respect to the Turner energy function are identified. Each potentially paired region within a multiple sequence alignment is scored using a function that combines both predicted free energy and sequence covariation with optimized weightings. High scoring regions are ranked and sequentially incorporated to define a growing secondary structure. Using a single set of optimized parameters, it is possible to accurately predict the foldings of several test RNAs defined previously by extensive phylogenetic and experimental data (including tRNA, 5 S rRNA, SRP RNA, tmRNA, and 16 S rRNA). The algorithm correctly predicts approximately 80% of the secondary structure. A range of parameters have been tested to define the minimal sequence information content required to accurately predict secondary structure and to assess the importance of individual terms in the prediction scheme. This analysis indicates that prediction accuracy most strongly depends upon covariational information and only weakly on the energetic terms. However, relatively few sequences prove sufficient to provide the covariational information required for an accurate prediction. Secondary structures can be accurately defined by alignments with as few as five sequences and predictions improve only moderately with the inclusion of additional sequences.     

Analysis of the structure of Tetrahymena nuclear RNAs in vivo: telomerase RNA, the self-splicing rRNA intron, and U2 snRNA.

Dimethyl sulfate modification of RNA in living Tetrahymena thermophila allowed assessment of RNA secondary structure and protein association. The self-splicing rRNA intron had the same methylation pattern in vivo as in vitro, indicating that the structures are equivalent and suggesting that this RNA is not stably associated with protein in the nucleolus. Methylation was consistent with the current secondary structure model. Much of telomerase RNA was protected from methylation in vivo, but the A's and C's in the template region were very reactive. Thus, most telomerase is not base paired to telomeres in vivo. Protein-free telomerase RNA adopts a structure different from that in vivo, especially in the template and pseudoknot regions. The U2 snRNA showed methylation protection at the Sm protein-binding sequence and the mRNA branch site recognition sequence. For both telomerase RNA and U2 snRNA, the in vivo methylation pattern corresponded much better to the structure determined by comparative sequence analysis than did the in vitro methylation pattern. Thus, as expected, comparative analysis gives the structure of the RNA in vivo.     

Free-energy landscape of a chameleon sequence in explicit water and its inherent alpha/beta bifacial property

A sequence in yeast MATalpha2/MCM1/DNA complex that folds into alpha-helix or beta-hairpin depending on the surroundings has been known as "chameleon" sequence. We obtained the free-energy landscape of this sequence by using a generalized-ensemble method, multicanonical molecular dynamics simulation, to sample the conformational space. The system was expressed with an all-atom model in explicit water, and the initial conformation for the simulation was a random one. The free-energy landscape demonstrated that this sequence inherently has an ability to form either alpha or beta structure: The conformational distribution in the landscape consisted of two alpha-helical clusters with different packing patterns of hydrophobic residues, and four beta-hairpin clusters with different strand-strand interaction patterns. Narrow pathways connecting the clusters were found, and analysis on the pathways showed that a compact structure formed at the N-terminal root of the chameleon sequence controls the cluster-cluster transitions. The free-energy landscape indicates that a small conditional change induces alpha-beta transitions. Additional unfolding simulations done with replacing amino acids showed that the chameleon sequence has an advantage to form an alpha-helix. Current study may be useful to understand the mechanism of diseases resulting from abnormal chain folding, such as amyloid disease.     

Novel GACG-hairpin pair motif in the 5'' untranslated region of type C retroviruses related to murine leukemia virus.

We searched for the presence of common RNA structural motifs in mammalian type C retroviruses related to murine leukemia viruses and the closely related avian spleen necrosis virus. A novel motif consisting of a pair of hairpins, called hairpin pair motif, was detected in the 5' untranslated regions of the genomes of these retroviruses. A combination of computational analyses that included the assessment of phylogenetic sequence conservation by multiple alignment, the search for regions with unusual RNA folding properties, and the analysis of RNA secondary structure by suboptimal free-energy calculations highlighted the significance of this hairpin pair motif. The hairpin pair motif encompasses 70 to 80 nucleotides between the splice donor site and the gag translational initiation codon of these viruses. The motif is composed of two adjacent hairpins both with a perfectly conserved GACG tetraloop. We propose that the novel GACG-hairpin pair motif described here constitutes an essential component of the regulatory machinery in these type C retroviruses.     

Boltzmann probability of RNA structural neighbors and riboswitch detection

MOTIVATION: We describe algorithms implemented in a new software package, RNAbor, to investigate structures in a neighborhood of an input secondary structure S of an RNA sequence s. The input structure could be the minimum free energy structure, the secondary structure obtained by analysis of the X-ray structure or by comparative sequence analysis, or an arbitrary intermediate structure. RESULTS: A secondary structure T of s is called a delta-neighbor of S if T and S differ by exactly delta base pairs. RNAbor computes the number (N(delta)), the Boltzmann partition function (Z(delta)) and the minimum free energy (MFE(delta)) and corresponding structure over the collection of all delta-neighbors of S. This computation is done simultaneously for all delta < or = m, in run time O (mn3) and memory O(mn2), where n is the sequence length. We apply RNAbor for the detection of possible RNA conformational switches, and compare RNAbor with the switch detection method paRNAss. We also provide examples of how RNAbor can at times improve the accuracy of secondary structure prediction. AVAILABILITY: http://bioinformatics.bc.edu/clotelab/RNAbor/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Secondary structure model for mouse beta Maj globin mRNA derived from enzymatic digestion data, comparative sequence and computer analysis.

A model for the secondary structure of mouse beta Maj globin messenger RNA is presented based on enzymatic digestion data, comparative sequence and computer analysis. Using 5'-32P-end-labeled beta globin mRNA as a substrate, single-stranded regions were determined with S1 and T1 nucleases and double-stranded regions with V1 ribonuclease from cobra venom. The structure data obtained for ca. 75% of the molecule was introduced into a computer algorithm which predicts secondary structures of minimum free energy consistent with the enzymatic data. Two prominent base paired regions independently derived by phylogenetic analysis were also present in the computer generated structure lending support for the model. An interesting feature of the model is the presence of long-range base pairing interactions which permit the beta globin mRNA to fold back on itself, thereby bringing the 5'- and 3'-noncoding regions within close proximity. This feature is consistent with data from other laboratories suggesting an interaction of the 5'- and 3'-domains in the mammalian globin mRNAs.     

Prediction of protein secondary structure content for the twilight zone sequences

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure.     

Identifying constraints on the higher-order structure of RNA: continued development and application of comparative sequence analysis methods.

Comparative sequence analysis addresses the problem of RNA folding and RNA structural diversity, and is responsible for determining the folding of many RNA molecules, including 5S, 16S, and 23S rRNAs, tRNA, RNAse P RNA, and Group I and II introns. Initially this method was utilized to fold these sequences into their secondary structures. More recently, this method has revealed numerous tertiary correlations, elucidating novel RNA structural motifs, several of which have been experimentally tested and verified, substantiating the general application of this approach. As successful as the comparative methods have been in elucidating higher-order structure, it is clear that additional structure constraints remain to be found. Deciphering such constraints requires more sensitive and rigorous protocols, in addition to RNA sequence datasets that contain additional phylogenetic diversity and an overall increase in the number of sequences. Various RNA databases, including the tRNA and rRNA sequence datasets, continue to grow in number as well as diversity. Described herein is the development of more rigorous comparative analysis protocols. Our initial development and applications on different RNA datasets have been very encouraging. Such analyses on tRNA, 16S and 23S rRNA are substantiating previously proposed associations and are now beginning to reveal additional constraints on these molecules. A subset of these involve several positions that correlate simultaneously with one another, implying units larger than a basepair can be under a phylogenetic constraint.