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At least 649 records · Page 36Linked to original sources
PubMed1990

A new U2 RNA secondary structure provided by phylogenetic analysis of trypanosomatid U2 RNAs.

A new model of U2 RNA secondary structure has been developed by comparing U2 RNA sequences from distantly related trypanosomatids, which process their RNAs by trans-splicing, and U2 RNAs from cis-splicing organisms. The trypanosomatid U2 RNA retains structural similarity in important functional domains of cis-splicing U2 RNAs yet differs from previous consensus models in that only two helices, rather than three, can form in the stem-loop II region. This alteration eliminates the capacity for pseudoknot formation and produces a single-stranded region 3' to stem-loop II, which may be accessible for snRNP protein binding. Trypanosomatid U2 RNAs differ in the putative branchpoint recognition sequences, which completely diverge from the conserved GUAGUA consensus of cis-splicing organisms.

Animals↗
PubMed2006

Combinatorics of saturated secondary structures of RNA.

Following Zuker (1986), a saturated secondary structure for a given RNA sequence is a secondary structure such that no base pair can be added without violating the definition of secondary structure, e.g., without introducing a pseudoknot. In the Nussinov-Jacobson energy model (Nussinov and Jacobson, 1980), where the energy of a secondary structure is -1 times the number of base pairs, saturated secondary structures are local minima in the energy landscape, hence form kinetic traps during the folding process. Here we present recurrence relations and closed form asymptotic limits for combinatorial problems related to the number of saturated secondary structures. In addition, Python source code to compute the number of saturated secondary structures having k base pairs can be found at the web servers link of bioinformatics.bc.edu/clotelab/.

Base Pairing↗
PubMed2002

Evolved RNA secondary structure and the rooting of the universal tree of life.

The origin and diversification of RNA secondary structure were traced using cladistic methods. Structural components were coded as polarized and ordered multi-state characters, following a model of character state transformation outlined by considerations in statistical mechanics. Several classes of functional RNA were analyzed, including ribosomal RNA (rRNA). Considerable phylogenetic signal was present in their secondary structure. The intrinsically rooted phylogenies reconstructed from evolved RNA structure depicted those derived from nucleic acid sequence at all taxonomical levels, and grouped organisms in concordance with traditional classification, especially in the archaeal and eukaryal domains. Natural selection appears therefore to operate early in the information flow that originates in sequence and ends in an adapted phenotype. When examining the hierarchical classification of the living world, phylogenetic analysis of secondary structure of the small and large rRNA subunits reconstructed a universal tree of life that branched in three monophyletic groups corresponding to Eucarya, Archaea, and Bacteria, and was rooted in the eukaryotic branch. Ribosomal characters involved in the translational cycle could be easily traced and showed that transfer RNA (tRNA) binding domains in the large rRNA subunit evolved concurrently with the rest of the rRNA molecule. Results suggest it is equally parsimonious to consider that ancestral unicellular eukaryotes or prokaryotes gave rise to all extant life forms and provide a rare insight into the early evolution of nucleic acid and protein biosynthesis.

Animals↗
PubMed1996

Inhibition of HIV-1 replication in viral mutants with altered TAR RNA stem structures.

Human immunodeficiency virus type 1 (HIV-1) Tat-mediated trans-activation requires the structural integrity of TAR RNA and the cooperative interaction of human host cell proteins. The TAR domain, minimally required for tat response, includes the Tat binding pyrimidine bulge, the TAR RNA upper stem, and the loop sequences. However, little is known about the significance of the 5'-stem structure of TAR in the regulation of viral growth. We designed viral mutations, specifically in the TAR RNA lower stem structure, and studied their effects on the kinetics of viral growth in T-lymphocyte cell lines and in activated human peripheral blood mononuclear cells. Mutations that destabilized the lower TAR stem structure inhibited viral growth to various degrees in different CD4+ T-cells. These results suggest that the structural integrity of the lower stem structure of TAR plays an important role in viral growth, presumably by binding to specific host cell proteins that stabilize Tat-TAR interactions.

Base Sequence↗
PubMed2002

Ebola virus VP30-mediated transcription is regulated by RNA secondary structure formation.

The nucleocapsid protein VP30 of Ebola virus (EBOV), a member of the Filovirus family, is known to act as a transcription activator. By using a reconstituted minigenome system, the role of VP30 during transcription was investigated. We could show that VP30-mediated transcription activation is dependent on formation of a stem-loop structure at the first gene start site. Destruction of this secondary structure led to VP30-independent transcription. Analysis of the transcription products of bicistronic minigenomes with and without the ability to form the secondary structure at the first transcription start signal revealed that transcription initiation at the first gene start site is a prerequisite for transcription of the second gene, independent of the presence of VP30. When the transcription start signal of the second gene was exchanged with the transcription start signal of the first gene, transcription of the second gene also was regulated by VP30, indicating that the stem-loop structure of the first transcription start site acts autonomously and independently of its localization on the RNA genome. Our results suggest that VP30 regulates a very early step of EBOV transcription, most likely by inhibiting pausing of the transcription complex at the RNA structure of the first transcription start site.

Animals↗
PubMed2006

A set of nearest neighbor parameters for predicting the enthalpy change of RNA secondary structure formation.

A complete set of nearest neighbor parameters to predict the enthalpy change of RNA secondary structure formation was derived. These parameters can be used with available free energy nearest neighbor parameters to extend the secondary structure prediction of RNA sequences to temperatures other than 37 degrees C. The parameters were tested by predicting the secondary structures of sequences with known secondary structure that are from organisms with known optimal growth temperatures. Compared with the previous set of enthalpy nearest neighbor parameters, the sensitivity of base pair prediction improved from 65.2 to 68.9% at optimal growth temperatures ranging from 10 to 60 degrees C. Base pair probabilities were predicted with a partition function and the positive predictive value of structure prediction is 90.4% when considering the base pairs in the lowest free energy structure with pairing probability of 0.99 or above. Moreover, a strong correlation is found between the predicted melting temperatures of RNA sequences and the optimal growth temperatures of the host organism. This indicates that organisms that live at higher temperatures have evolved RNA sequences with higher melting temperatures.

Algorithms↗
PubMed2000

Stabilization of RNA tertiary structure by monovalent cations.

The effects of monovalent cations (Li(+), Na(+), K(+), Rb(+), Cs(+), and NH4(+)) on the thermal stability of RNA tertiary structure were investigated by UV melting. We show that with the RNA used here (nucleotides 1051-1108 of Escherichia coli 23 S rRNA with four base substitutions), monovalent cations and Mg(2+) compete in stabilizing the RNA tertiary structure, and that the competition takes place between two boundaries: one where Mg(2+) concentration is zero and the other where it is maximally stabilizing ("saturating"). The pattern of competition is the same for all monovalent cations and depends on the cation's ability to displace Mg(2+) from the RNA, its ability to stabilize tertiary structure in the absence of Mg(2+), and its ability to stabilize tertiary structure at saturating Mg(2+) concentrations. The stabilizing ability of a monovalent cation depends on its unhydrated ionic radius, and at a low monovalent cation concentration and saturating Mg(2+), there is a (calculated) net release of a single monovalent cation/RNA molecule when tertiary structure is denatured. The implications are that under these conditions there is at least one binding site for monovalent cations on the RNA, the site is specifically associated with formation of stable tertiary structure, K(+) is the most effective of the tested cations, and Mg(2+) appears ineffective at this site. At high ionic strength, and in the absence of Mg(2+), stabilization of tertiary structure is still monovalent-cation specific and ionic-radius dependent, but a larger number of cations ( approximately eight) are released upon RNA tertiary structure denaturation, and NH(4)(+) appears to be the most effective cation in stabilizing tertiary structure under these conditions. In the majority of the experiments, methanol was added as a cosolvent to the buffer. Its use allowed the examination of the behavior of monovalent ions under conditions where their effects would otherwise have been too weak to be observed. Methanol stabilizes tertiary but not secondary structure of the RNA. There was no evidence that it either causes qualitative changes in cation-binding properties of the RNA or a change in the pattern of monovalent cation/Mg(2+) competition.

Base Sequence↗
PubMed1995

RNA secondary structure and binding sites for gag gene products in the 5' packaging signal of human immunodeficiency virus type 1.

The selective encapsidation of retroviral RNA requires sequences in the Gag protein, as well as a cis-acting RNA packaging signal (psi site) near the 5' end of the genomic transcript. Gag protein of human immunodeficiency virus type 1 (HIV-1) has recently been found to bind specifically to the HIV-1 psi element in vitro. Here we report studies aimed at mapping features within the genetically defined psi locus that are required for binding of HIV-1 Gag or of its processed nucleocapsid derivative. The full-length HIV-1 Gag (p55) and nucleocapsid (p15) sequences were expressed as glutathione S-transferase (GST) fusion proteins in Escherichia coli. In a gel shift assay containing excess competitor tRNA, affinity-purified GST-p15 and GST-p55 proteins bound to a 206-nucleotide psi RNA element spanning the major splice donor and gag start codons but did not bind to antisense psi transcripts. Quantitative filter-binding assays revealed that both GST-p55 and GST-p15 bound to this RNA sequence with identical affinities (apparent Kd congruent to 5 x 10(-8) M), indicating that all major determinants of psi binding affinity reside within the nucleocapsid portion of Gag. Chemical and RNase accessibility mapping, coupled with computerized sequence analysis, suggested a model for psi RNA structure comprising four independent stem-loops. Filter-binding studies revealed that RNAs corresponding to three of these hypothetical stem-loops can each function as a independent Gag binding site and that each is bound with approximately fourfold-lower apparent affinity than the full-length psi locus. Interaction of Gag with these regions is likely to play a major role in directing HIV-1 RNA encapsidation in vivo.

Base Sequence↗
PubMed2003

RNAsoft: A suite of RNA secondary structure prediction and design software tools.

DNA and RNA strands are employed in novel ways in the construction of nanostructures, as molecular tags in libraries of polymers and in therapeutics. New software tools for prediction and design of molecular structure will be needed in these applications. The RNAsoft suite of programs provides tools for predicting the secondary structure of a pair of DNA or RNA molecules, testing that combinatorial tag sets of DNA and RNA molecules have no unwanted secondary structure and designing RNA strands that fold to a given input secondary structure. The tools are based on standard thermodynamic models of RNA secondary structure formation. RNAsoft can be found online at http://www.RNAsoft.ca.

Base Sequence↗
PubMed2004

A 3D graphical representation of RNA secondary structures.

In this paper, we proposed a 3-D graphical representation of RNA secondary structures. Based on this representation, we outline an approach by constructing a 3-component vector whose components are the normalized leading eigenvalues of the L/L matrices associated with RNA secondary structure. The examination of similarities/dissimilarities among the secondary structure at the 3'-terminus of different viruses illustrates the utility of the approach.

Algorithms↗
PubMed2002

RNAStudio, a full-featured object-oriented program for visualizing RNA secondary structures.

The function of a RNA molecule relies on the underlying physical mechanisms and geometric properties in terms of secondary or tertiary structure, in which one of the most important properties is topological connectivity. RNAStudio, available at http://rnastudio.51.net, brings the convenience of Windows to the representation of RNA folding topology including the sections mentioned below.

Internet↗
PubMed1996

Classification of RNA secondary structures using the techniques of cluster analysis.

An RNA molecule has a lot of suboptimal secondary structures. Some of these suboptimal structures are very similar and some are entirely different. In some cases, the free energy of these suboptimal structures does not differ much from that of the optimal structure. In order to characterize this relationship more clearly, we defined a metric among the secondary structures by the unweighted pair group method, using the arithmetic average and extended this metric to the sets of secondary structures. We report two applications of this metric. First, we developed a method for the classification of these suboptimal secondary structures of a given RNA sequence. Results of classification are presented with the secondary structures of cadang-cadang coconut viroid, potato spindle tuber viroid and polio virus as examples. Second, we applied this metric to the classification of the secondary structures derived from a set of mutant RNA sequences. We discuss that the mutation of a given RNA sequence changes not only the optimal secondary structure, but also the population of the cluster of the secondary structures.

Animals↗
PubMed1998

Exploration of metal ion binding sites in RNA folds by Brownian-dynamics simulations.

BACKGROUND: Metal ions participate in the three-dimensional folding of RNA and provide active centers in catalytic RNA molecules. The positions of metal ions are known for a few RNA structures determined by X-ray crystallography. In addition to the crystallographically identified sites, solution studies point to many more metal ion binding sites around structured RNAs. Metal ions are also present in RNA structures determined by nuclear magnetic resonance (NMR) spectroscopy, but the positions of the ions are usually not revealed. RESULTS: A novel method for predicting metal ion binding sites in RNA folds has been successfully applied to a number of different RNA structures. The method is based on Brownian-dynamics simulations of cations diffusing under the influence of random Brownian motion within the electrostatic field generated by the static three-dimensional fold of an RNA molecule. In test runs, the crystallographic positions of Mg2+ ions were reproduced with deviations between 0.3 and 2.7 A for several RNA molecules for which X-ray structures are available. In addition to the crystallographically identified metal ions, more binding sites for cations were revealed: for example, tRNAs were shown to bind more than ten Mg2+ ions in solution. Predictions for metal ion binding sites in four NMR structures of RNA molecules are discussed. CONCLUSIONS: The successful reproduction of experimentally observed metal ion binding sites demonstrates the efficiency of the prediction method. A promising application of the method is the prediction of cation-binding sites in RNA solution structures, determined by NMR.

Base Sequence↗
PubMed2005

Psoralen photocrosslinking, a tool to study the chromatin structure of RNA polymerase I--transcribed ribosomal genes.

The chromatin structure of RNA polymerase I--transcribed ribosomal DNA (rDNA) is well characterized. In most organisms, i.e., lower eukaryotes, plants, and animals, only a fraction of ribosomal genes are transcriptionally active. At the chromatin level inactive rDNA is assembled into arrays of nucleosomes, whereas transcriptionally active rDNA does not contain canonical nucleosomes. To separate inactive (nucleosomal) and active (non-nucleosomal) rDNA, the technique of psoralen photocrosslinking has been used successfully both in vitro and in vivo. In Saccharomyces cerevisiae, the structure of rDNA chromatin has been particularly well studied during transcription and during DNA replication. Thus, the yeast rDNA locus has become a good model system to study the interplay of all nuclear DNA processes and chromatin. In this review we focused on the studies of chromatin in ribosomal genes and how these results have helped to address the fundamental question: What is the structure of chromatin in the coding regions of genes?

Chromatin↗
PubMed2003

Local similarity in RNA secondary structures.

We present a systematic treatment of alignment distance and local similarity algorithms on trees and forests. We build upon the tree alignment algorithm for ordered trees given by Jiang et. al (1995) and extend it to calculate local forest alignments, which is essential for finding local similar regions in RNA secondary structures. The time complexity of our algorithm is O(|F(1)| |F(2) deg(F(1)) deg(F(2)) (deg(F(1)) + deg(F(2))) where |F(i)| is the number of nodes in forest F(i) and deg (F(i)) is the degree of F(i). We provide carefully engineered dynamic programming implementations using dense, two-dimensional tables which considerably reduces the space requirement. We suggest a new representation of RNA secondary structures as forests that allow reasonable scoring of edit operations on RNA secondary structures. The comparison of RNA secondary structures is facilitated by a new visualization technique for RNA secondary structure alignments. Finally, we show how potential regulatory motifs can be discovered solely by their structural preservation, and independent of their sequence conservation and position.

Algorithms↗
PubMed2000

Circles: automating the comparative analysis of RNA secondary structure.

UNLABELLED: Circles is a program for inferring RNA secondary structure using maximum weight matching. The program can read in an alignment in FASTA, ClustalW, or NEXUS format, compute a maximum weight matching, and export one or more secondary structures in various file formats. AVAILABILITY: The program is available at no cost from http://taxonomy.zoology.gla.ac.uk/rod/circles/ and requires Windows 95/98/NT. CONTACT: r.page@bio.gla.ac.uk

Base Sequence↗
PubMed2000

Structure, stability and function of RNA pseudoknots involved in stimulating ribosomal frameshifting.

Programmed -1 ribosomal frameshifting has become the subject of increasing interest over the last several years, due in part to the ubiquitous nature of this translational recoding mechanism in pathogenic animal and plant viruses. All cis-acting frameshift signals encoded in mRNAs are minimally composed of two functional elements: a heptanucleotide "slippery sequence" conforming to the general form X XXY YYZ, followed by an RNA structural element, usually an H-type RNA pseudoknot, positioned an optimal number of nucleotides (5 to 9) downstream. The slippery sequence itself promotes a low level ( approximately 1 %) of frameshifting; however, downstream pseudoknots stimulate this process significantly, in some cases up to 30 to 50 %. Although the precise molecular mechanism of stimulation of frameshifting remains poorly understood, significant advances have been made in our knowledge of the three-dimensional structures, thermodynamics of folding, and functional determinants of stimulatory RNA pseudoknots derived from the study of several well-characterized frameshift signals. These studies are summarized here and provide new insights into the structural requirements and mechanism of programmed -1 ribosomal frameshifting.

Base Sequence↗
PubMed2004

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure.

A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs.

Algorithms↗