Site-specific incorporation of nitroxide spin-labels into internal sites of the TAR RNA; structure-dependent dynamics of RNA by EPR spectroscopy.
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Binding Sites↗
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Improved predictions of secondary structures for RNA.
The accuracy of computer predictions of RNA secondary structure from sequence data and free energy parameters has been increased to roughly 70%. Performance is judged by comparison with structures known from phylogenetic analysis. The algorithm also generates suboptimal structures. On average, the best structure within 10% of the lowest free energy contains roughly 90% of phylogenetically known helixes. The algorithm does not include tertiary interactions or pseudoknots and employs a crude model for single-stranded regions. The only favorable interactions are base pairing and stacking of terminal unpaired nucleotides at the ends of helixes. The excellent performance is consistent with these interactions being the primary interactions determining RNA secondary structure.
Base Composition↗
Secondary structure maps of ribosomal RNA and DNA. I. Processing of Xenopus laevis ribosomal RNA and structure of single-stranded ribosomal DNA.
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Animals↗
RNAMotif, an RNA secondary structure definition and search algorithm.
RNA molecules fold into characteristic secondary and tertiary structures that account for their diverse functional activities. Many of these RNA structures are assembled from a collection of RNA structural motifs. These basic building blocks are used repeatedly, and in various combinations, to form different RNA types and define their unique structural and functional properties. Identification of recurring RNA structural motifs will therefore enhance our understanding of RNA structure and help associate elements of RNA structure with functional and regulatory elements. Our goal was to develop a computer program that can describe an RNA structural element of any complexity and then search any nucleotide sequence database, including the complete prokaryotic and eukaryotic genomes, for these structural elements. Here we describe in detail a new computational motif search algorithm, RNAMotif, and demonstrate its utility with some motif search examples. RNAMotif differs from other motif search tools in two important aspects: first, the structure definition language is more flexible and can specify any type of base-base interaction; second, RNAMotif provides a user controlled scoring section that can be used to add capabilities that patterns alone cannot provide.
3' Untranslated Regions↗
The solution structure of an RNA loop-loop complex: the ColE1 inverted loop sequence.
BACKGROUND: Replication of the ColE1 plasmid of Escherichia coli is regulated by the interaction of sense and antisense plasmid-encoded transcripts. The antisense RNA I negatively regulates the replication of the plasmid by duplex formation with complementary RNA II. The interaction is initiated by the formation of a double helix between seven-nucleotide loops from each RNA and is stabilized by binding of the RNA one modulator (ROM) protein. The ROM protein is thought to recognize a specific RNA structure, regardless of sequence. RESULTS: The solution structure of a loop-loop complex between model RNA hairpins that resemble RNA I and RNA II has been determined by nuclear magnetic resonance spectroscopy. The model hairpins have loop sequences inverted 5' to 3' relative to the wild-type sequence and were chosen because of their complex's slow dissociation in comparison to the wild type. The complex has continuous stacking from the 3'-side of one stem helix through the loop-loop helix to the other stem helix. One residue from each hairpin has a unique phosphodiester bond which bridges and narrows the major groove. These bridging phosphates are in close proximity to the phosphate groups of the adjacent bases, forming unique structural motifs called phosphate clusters. The purine residue at the 3'-end of the loop-loop helix of one RNA stacks on a purine residue on the 5'-side of the other RNA stem, and there are strong cross-strand stacking interactions between guanine bases in the stem helices adjacent to the loops. CONCLUSIONS: Unique distortions, such as the strong bend and the phosphate clusters flanking the major groove of the loop-loop helix, provide an attractive nonsequence-specific structural feature for recognition by the ROM protein. The structure provides a basis for rationalizing the sequence dependence of the stability of loop-loop interaction.
Bacterial Proteins↗
Structural requirements for RNA editing in glutamate receptor pre-mRNAs by recombinant double-stranded RNA adenosine deaminase.
Pre-mRNAs for brain-expressed ionotropic glutamate receptor subunits undergo RNA editing by site-specific adenosine deamination, which alters codons for molecular determinants of channel function. This nuclear process requires double-stranded RNA structures formed by exonic and intronic sequences in the pre-mRNA and is likely to be catalyzed by an adenosine deaminase that recognizes these structures as a substrate. DRADA, a double-stranded RNA adenosine deaminase, is a candidate enzyme for L-glutamate-activated receptor channel (GluR) pre-mRNA editing. We show here that DRADA indeed edits GluR pre-mRNAs, but that it displays selectivity for certain editing sites. Recombinantly expressed DRADA, both in its full-length form and in an N-terminally truncated version, edited the Q/R site in GluR6 pre-mRNA and the R/G site but not the Q/R site of GluR-B pre-mRNA. This substrate selectivity correlated with the base pairing status and sequence environment of the editing-targeted adenosines. The Q/R site of GluR-B pre-mRNA was edited by an activity partially purified from HeLa cells and thus differently structured editing sites in GluR pre-mRNAs appear to be substrates for different enzymatic activities.
Adenosine↗
A statistical sampling algorithm for RNA secondary structure prediction.
An RNA molecule, particularly a long-chain mRNA, may exist as a population of structures. Further more, multiple structures have been demonstrated to play important functional roles. Thus, a representation of the ensemble of probable structures is of interest. We present a statistical algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures. The forward step of the algorithm computes the equilibrium partition functions of RNA secondary structures with recent thermodynamic parameters. Using conditional probabilities computed with the partition functions in a recursive sampling process, the backward step of the algorithm quickly generates a statistically representative sample of structures. With cubic run time for the forward step, quadratic run time in the worst case for the sampling step, and quadratic storage, the algorithm is efficient for broad applicability. We demonstrate that, by classifying sampled structures, the algorithm enables a statistical delineation and representation of the Boltzmann ensemble. Applications of the algorithm show that alternative biological structures are revealed through sampling. Statistical sampling provides a means to estimate the probability of any structural motif, with or without constraints. For example, the algorithm enables probability profiling of single-stranded regions in RNA secondary structure. Probability profiling for specific loop types is also illustrated. By overlaying probability profiles, a mutual accessibility plot can be displayed for predicting RNA:RNA interactions. Boltzmann probability-weighted density of states and free energy distributions of sampled structures can be readily computed. We show that a sample of moderate size from the ensemble of an enormous number of possible structures is sufficient to guarantee statistical reproducibility in the estimates of typical sampling statistics. Our applications suggest that the sampling algorithm may be well suited to prediction of mRNA structure and target accessibility. The algorithm is applicable to the rational design of small interfering RNAs (siRNAs), antisense oligonucleotides, and trans-cleaving ribozymes in gene knock-down studies.
Algorithms↗
Classification of 3D structural character of RNA by hydrogen bond and base stacking.
We are developing a computational system to classify RNA structures by its structural character. Here, an improved grouping algorithm was introduced to the system and the base-stacking pattern (BSP) is used as a criterion for the classification in addition to hydrogen-bond pattern (HBP). 279 conformers of 15 mer RNA hairpin were classified into 89 and 36 groups by HBP and BSP, respectively, suggesting that HBP represents conformational character better than BSP.
Algorithms↗
Molecular modelling of the 3-D structure of RNA tetraloops with different nucleotide sequences.
One surprisingly common element of RNA secondary structure consists of a hairpin capped by a four-base loop (or the tetraloop). Recently the 3-D structures of two RNA-tetraloops have been determined by NMR-studies. Both structures have a similar architecture: the first and the last bases of the loop form a hydrogen bonded pair which is stacked on the stem base pair. We have analysed the ability of tetraloops, with the other combinations of the first and the fourth bases, to adopt such a 'diloop' conformation using computer modelling. The analysis has shown that the 'diloop' conformation has many covalent and steric constraints which give a possibility for reliable structural predictions. As a result, a set of the tetraloop 3-D structures in which hydrogen bonded pairing of the first and the last bases does not cause covalent and steric hindrances has been selected. In most cases several predicted 3-D structures corresponded to one tetraloop sequence. Taking into consideration the folding pathway of RNA hairpins we have resolved this ambiguity and predicted the most probable 3-D structure for every possible nucleotide sequence of the tetraloop. On the basis of these results a conclusion has been drawn on the possible reasons of the tetraloop phylogenetic preference.
Computer Simulation↗
"Well-determined" regions in RNA secondary structure prediction: analysis of small subunit ribosomal RNA.
Recent structural analyses of genomic RNAs from RNA coliphages suggest that both well-determined base paired helices and well-determined structural domains that are identified by "energy dot plot" analysis using the RNA folding package mfold, are likely to be predicted correctly. To test these observations with another group of large RNAs, we have analyzed 15 ribosomal RNAs. Published secondary structure models that were derived by comparative sequence analysis were used to evaluate the predicted structures. Both the optimal predicted fold and the predicted "energy dot plot" of each sequence were examined. Each prediction was obtained from a single computer run on an entire ribosomal RNA sequence. All predicted base pairs in optimal foldings were examined for agreement with proven base pairs in the comparative models. Our analyses show that the overall correspondence between the predicted and comparative models varied for different RNAs and ranges from a low of 27% to high of 70%, with a mean value of 49%. The correspondence improves to a mean value of 81% when the analysis is limited to well-determined helices. In addition to well-determined helices, large well-determined structural domains can be observed in "energy dot plots" of some 16S ribosomal RNAs. The predicted domains correspond closely with structural domains that are found by the comparative method in the same RNAs. Our analyses also show that measuring the agreement between predicted and comparative secondary structure models underestimates the reliability of structural prediction by mfold.
Base Sequence↗
Pure multiple RNA secondary structure alignments: a progressive profile approach.
In functional, noncoding RNA, structure is often essential to function. While the full 3D structure is very difficult to determine, the 2D structure of an RNA molecule gives good clues to its 3D structure, and for molecules of moderate length, it can be predicted with good reliability. Structure comparison is, in analogy to sequence comparison, the essential technique to infer related function. We provide a method for computing multiple alignments of RNA secondary structures under the tree alignment model, which is suitable to cluster RNA molecules purely on the structural level, i.e., sequence similarity is not required. We give a systematic generalization of the profile alignment method from strings to trees and forests. We introduce a tree profile representation of RNA secondary structure alignments which allows reasonable scoring in structure comparison. Besides the technical aspects, an RNA profile is a useful data structure to represent multiple structures of RNA sequences. Moreover, we propose a visualization of RNA consensus structures that is enriched by the full sequence information.
5' Untranslated Regions↗
Base-pair probability profiles of RNA secondary structures.
Dynamic programming algorithms are able to predict optimal and suboptimal secondary structures of RNA. These suboptimal or alternative secondary structures are important for the biological function of RNA. The distribution of secondary structures present in solution is governed by the thermodynamic equilibrium between the different structures. An algorithm is presented which approximates the total partition function by a Boltzmann-weighted summation of optimal and suboptimal secondary structures at several temperatures. A clear representation of the equilibrium distribution of secondary structures is derived from a two-dimensional bonding matrix with base-pairing probability as the third dimension. The temperature dependence of the equilibrium distribution gives the denaturation behavior of the nucleic acid, which may be compared to experimental optical denaturation curves after correction for the hypochromicities of the different base-pairs. Similarly, temperature-induced mobility changes detected in temperature-gradient gel electrophoresis of nucleic acids may be interpreted on the basis of the temperature dependence of the equilibrium distribution. Results are illustrated for natural circular and synthetic linear potato spindle tuber viroid RNA respectively, and are compared to experimental data.
Algorithms↗
Solution structure of lariat RNA by 500 MHz NMR spectroscopy and molecular dynamics studies in water.
A 500 MHz NMR study of the lariat RNA tetramer 1 and pentamer 2 mimicking the naturally occurring lariat RNA is reported. The conformational properties of 1 and 2 were compared with those of a linear branched RNA tetramer 3, which show that the conformational features of the two lariat RNAs, 1 and 2, are quite constrained and significantly different from those observed for the linear branched RNA tetramer 3. The conformation of all sugar residues forming the lariat ring in 1 and 2 are locked in a rigid South-type conformation. All residues in both lariat RNAs have a high population of gamma+ (67-85%) and beta t (95-100%) rotamers except guanosine where the gamma+ population is low. The conformation around the glycosidic bond is anti for all residues except for guanosine where NOE data indicates an equilibrium of syn<-->anti. In both lariat RNAs, 1 and 2, the temperature dependent 1H and 31P chemical shifts as well as the oligomerization shifts, with respect to adenosine 2',3',5'-triethyl-phosphate (Sund et al., 1992, Tetrahedron 48, 695) suggests that the 3'-->5' linked U4 or C4 residue is stacked on guanosine. Subsequently, 1H-1H, 1H-31P and 13C-31P coupling constants derived torsional constraints were used for molecular dynamics study in water with counter sodium ions for a total of 226 ps. The MD simulations were first carried out with harmonic torsional constraints which were derived from J couplings (0-86 ps) and then completely without constraints (96-226 ps). The lack of any major changes in the conformation of the two lariat-RNA structures upon releasing the NMR constraints indicate that the conformers generated in the MD simulation in water agree well with the structural features suggested by experimental observables. This means that the ensemble of conformers generated during the MD trajectory of 226 ps are not artificially held in these conformations due to the NMR constraints, suggesting that these conformers can be considered to be good representatives of the actual NMR observed solution structures.
Base Sequence↗
Synthetic oligoribonucleotides carrying site-specific modifications for RNA structure-function analysis.
Synthetic oligoribonucleotides have become increasingly valuable in studies of RNA structure and function. A range of nucleotide analogues is available which carry modifications in the base, sugar or phosphate moieties. Such analogues have been incorporated into synthetic RNA structures to eliminate or alter individual functional groups in the RNA which potentially can take part in hydrogen-bonding or other non-covalent interactions. Comparisons of the properties of the modified RNAs with unmodified RNA models allow conclusions to be drawn concerning the importance or otherwise of specific functional groups within the RNA. These methods have been applied to studies of RNA interactions with proteins, RNA catalysis and RNA structure.
Base Sequence↗
A method for rapid similarity analysis of RNA secondary structures.
BACKGROUND: Owing to the rapid expansion of RNA structure databases in recent years, efficient methods for structure comparison are in demand for function prediction and evolutionary analysis. Usually, the similarity of RNA secondary structures is evaluated based on tree models and dynamic programming algorithms. We present here a new method for the similarity analysis of RNA secondary structures. RESULTS: Three sets of real data have been used as input for the example applications. Set I includes the structures from 5S rRNAs. Set II includes the secondary structures from RNase P and RNase MRP. Set III includes the structures from 16S rRNAs. Reasonable phylogenetic trees are derived for these three sets of data by using our method. Moreover, our program runs faster as compared to some existing ones. CONCLUSION: The famous Lempel-Ziv algorithm can efficiently extract the information on repeated patterns encoded in RNA secondary structures and makes our method an alternative to analyze the similarity of RNA secondary structures. This method will also be useful to researchers who are interested in evolutionary analysis.
Algorithms↗
Generic properties of combinatory maps: neutral networks of RNA secondary structures.
Random graph theory is used to model and analyse the relationships between sequences and secondary structures of RNA molecules, which are understood as mappings from sequence space into shape space. These maps are non-invertible since there are always many orders of magnitude more sequences than structures. Sequences folding into identical structures form neutral networks. A neutral network is embedded in the set of sequences that are compatible with the given structure. Networks are modeled as graphs and constructed by random choice of vertices from the space of compatible sequences. The theory characterizes neutral networks by the mean fraction of neutral neighbors (lambda). The networks are connected and percolate sequence space if the fraction of neutral nearest neighbors exceeds a threshold value (lambda > lambda *). Below threshold (lambda < lambda *), the networks are partitioned into a largest "giant" component and several smaller components. Structures are classified as "common" or "rare" according to the sizes of their pre-images, i.e. according to the fractions of sequences folding into them. The neutral networks of any pair of two different common structures almost touch each other, and, as expressed by the conjecture of shape space covering sequences folding into almost all common structures, can be found in a small ball of an arbitrary location in sequence space. The results from random graph theory are compared to data obtained by folding large samples of RNA sequences. Differences are explained in terms of specific features of RNA molecular structures.
Base Sequence↗
The role of a metastable RNA secondary structure in hepatitis delta virus genotype III RNA editing.
RNA editing plays a critical role in the life cycle of hepatitis delta virus (HDV). The host editing enzyme ADAR1 recognizes specific RNA secondary structure features around the amber/W site in the HDV antigenome and deaminates the amber/W adenosine. A previous report suggested that a branched secondary structure is necessary for editing in HDV genotype III. This branched structure, which is distinct from the characteristic unbranched rod structure required for HDV replication, was only partially characterized, and knowledge concerning its formation and stability was limited. Here, we examine the secondary structures, conformational dynamics, and amber/W site editing of HDV genotype III RNA using a miniaturized HDV genotype III RNA in vitro. Computational analysis of this RNA using the MPGAfold algorithm indicated that the RNA has a tendency to form both metastable and stable unbranched secondary structures. Moreover, native polyacrylamide gel electrophoresis demonstrated that this RNA forms both branched and unbranched rod structures when transcribed in vitro. As predicted, the branched structure is a metastable structure that converts readily to the unbranched rod structure. Only branched RNA was edited at the amber/W site by ADAR1 in vitro. The structural heterogeneity of HDV genotype III RNA is significant because not only are both conformations of the RNA functionally important for viral replication, but the ratio of the two forms could modulate editing by determining the amount of substrate RNA available for modification.
Adenosine Deaminase↗
Vienna RNA secondary structure server.
The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently offers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences and the design of sequences that will fold into a predefined structure. All three services can be accessed via the Vienna RNA web server at http://rna.tbi.univie.ac.at/.
Algorithms↗