Higher plant 5S rRNAs share common secondary and tertiary structure. A new three domains model

A new model of secondary and tertiary structure of higher plant 5S RNA is proposed. It consists of three helical domains: domain alpha includes stem I; domain beta contains stems II and III and loops B and C; domain gamma consists of stems IV and V and loops D and E. Except for, presumably, a canonical RNA-A like domain alpha, the two remaining domains apparently adopt a perturbed RNA-A structure due to irregularities within internal loops B and E and three bulges occurring in the model. Bending of RNA could bring loops B and E and/or C and D closer making tertiary interactions likely. The model differs from that suggested for eukaryotic 5S rRNA, by organization of domain gamma. Our model is based on the results of partial digestion obtained with single- and double-strand RNA specific nucleases. The proposed secondary structure is strongly supported by the observation that crude plant 5S rRNA contains abundant RNA, identified as domain gamma of 5S rRNA. Presumably it is excised from the 5S rRNA molecule by a specific nuclease present in lupin seeds. Experimental results were confirmed by computer-aided secondary structure prediction analysis of all higher plant 5S rRNAs. Differences observed between earlier proposed models and our proposition are discussed.     

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

Background

A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1.

Results

The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases.

Conclusion

Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.     

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.     

Tertiary structure and computer modeling of plant 5S ribosomal RNA

A new model of secondary and tertiary structure of higher plant 5S rRNA is proposed. It consists of three domains. Domain alpha includes stem I and loop A; domain beta contains stems II and III and loops B and C; domain gamma consists of stems IV and V and loops D and E. We propose that the domains beta and gamma adopt RNA-A like structure due to irregularities caused by the different in size internal loops B and E and the bulges occurring in the model. A suggested bending of RNA could bring single stranded fragments of domains beta and gamma close enough to each other to allow tertiary interactions. The new model of plant 5S rRNA differs from those suggested previously for eukaryotic 5S rRNA, by arrangement of the domains beta and gamma and the base pairing scheme of domain gamma. The model is based on our results of partial digestion obtained with single and double strand specific nucleases. The experimental results were confirmed by computer aided secondary structure prediction analysis of all higher plant 5S rRNAs and computer modeling using energy minimalization approach. Further support of our model have been provided by experiments including alpha sarcin, ribonuclease H and chemical modifications.     

Unfolding of the tertiary structure of specific tRNA and ribosomal 5S RNA from plants as studied with hydroxyl radicals.

Ribosomal 5S RNA is present in all eubacterial and eukaryotic ribosomes. Despite a large amount of experimental data on the primary and secondary structures of these types of molecules, details of their tertiary structure and their precise function in protein biosynthesis are still not known. Recently we have proposed a new model for the tertiary structure of plant 5S rRNA. In this study we applied the Fe(II)-mediated cleavage reaction to test the model. The data presented here provide experimental evidence that in the 5S rRNA molecule only a few nucleotides are buried in the tertiary structure. Similar experiments performed with methionine initiator tRNA gave results which imply the difference in its structure when compared with the X-ray structure of yeast tRNAPhe.     

一种预测单链RNA分子二级结构的计算机算法

本文给出了一个利用已知能量数据构成具有最小自由能的单链RNA分子二级结构的计算机算法,并给出了此算法的可行性证明和应用实例。     

Effect of point mutations on 5.8S ribosomal ribonucleic acid secondary structure and the 5.8S--28S ribosomal ribonucleic acid junction

Naturally occurring differences in the nucleotide sequences of 5.8S ribosomal ribonucleic acids (rRNAs) from a variety of organisms have been used to study the role of specific nucleotides in the secondary structure and intermolecular interactions of this RNA. Significant differences in the electrophoretic mobilities of free 5.8S RNAs and the thermal stabilities of 5.8S--28S rRNA complexes were observed even in such closely related sequences as those of man, rat, turtle, and chicken. A single base transition from a guanylic acid residue in position 2 in mammalian 5.8S rRNA to an adenylic acid residue in turtle and chicken 5.8S rRNA results both in a more open molecular conformation and in a 5.8S--28S rRNA junction which is 3.5 degrees C more stable to thermal denaturation. Other changes such as the deletion of single nucleotides from either the 5' or the 3' terminals have no detectable effect on these features. The results support secondary structure models for free 5.8S rRNA in which the termini interact to various degrees and 5.8S--28S rRNA junctions in which both termini of the 5.8S molecule interact with the cognate high molecular weight RNA component.     

Constraining ribosomal RNA conformational space

Despite the potential for many possible secondary-structure conformations, the native sequence of ribosomal RNA (rRNA) is able to find the correct and universally conserved core fold. This study reports a computational analysis investigating two mechanisms that appear to constrain rRNA secondary-structure conformational space: ribosomal proteins and rRNA sequence composition. The analysis was carried out by using rRNA–ribosomal protein interaction data for the Escherichia coli 16S rRNA and free energy minimization software for secondary-structure prediction. The results indicate that selection pressures on rRNA sequence composition and ribosomal protein–rRNA interaction play a key role in constraining the rRNA secondary structure to a single stable form.     

A unique secondary folding pattern for 5S RNA corresponds to the lowest energy homologous secondary structure in 17 different prokaryotes.

A general secondary structure is proposed for the 5S RNA of prokaryotic ribosomes, based on helical energy filtering calculations. We have considered all secondary structures that are common to 17 different prokaryotic 5S RNAs and for each 5S sequence calculated the (global) minimum energy secondary structure (300,000 common structures are possible for each sequence). The 17 different minimum energy secondary structures all correspond, with minor differences, to a single, secondary structure model. This is strong evidence that this general 5S folding pattern corresponds to the secondary structure of the functional 5S rRNA. The general 5S secondary structure is forked and in analogy with the cloverleaf of tRNA is named the "wishbone" model. It constant 8 double helical regions; one in the stem, four in the first, or constant arm, and three in the second arm. Four of these double helical regions are present in a model earlier proposed (1) and four additional regions not proposed by them are presented here. In the minimum energy general structure, the four helices in the constant arm are exactly 15 nucleotide pairs long. These helices are stacked in the sequences from gram-positive bacteria and probably stacked in gram-negative sequences as well. In sequences from gram-positive bacteria the length of the constant arm is maintained at 15 stacked pairs by an unusual minimum energy interaction involving a C26-G57 base pair intercalated between two adjacent helical regions.     

Improvement of RNA secondary structure prediction using RNase H cleavage and randomized oligonucleotides

RNA secondary structure prediction using free energy minimization is one method to gain an approximation of structure. Constraints generated by enzymatic mapping or chemical modification can improve the accuracy of secondary structure prediction. We report a facile method that identifies single-stranded regions in RNA using short, randomized DNA oligonucleotides and RNase H cleavage. These regions are then used as constraints in secondary structure prediction. This method was used to improve the secondary structure prediction of Escherichia coli 5S rRNA. The lowest free energy structure without constraints has only 27% of the base pairs present in the phylogenetic structure. The addition of constraints from RNase H cleavage improves the prediction to 100% of base pairs. The same method was used to generate secondary structure constraints for yeast tRNA^Phe, which is accurately predicted in the absence of constraints (95%). Although RNase H mapping does not improve secondary structure prediction, it does eliminate all other suboptimal structures predicted within 10% of the lowest free energy structure. The method is advantageous over other single-stranded nucleases since RNase H is functional in physiological conditions. Moreover, it can be used for any RNA to identify accessible binding sites for oligonucleotides or small molecules.     

RNA二级结构的最小自由能算法

RNA(即tRNA,rRNA,mRNA和SnRNA)有两大主要功能：一是某些病毒的遗传物质;二是参与蛋白质的合成,这些与细胞分化、代谢、记忆的储存等有重要关系,这些功能与RNA二级结构的稳定性。自由能密切相关．常用的计算自由能的方法有热力学微扰法及热力学微积分法等．本文以寻找最小自由能二级结构为目的,给出了RNA二级结构的最小自由能算法,该算法的时间复杂性不超过O(n^4)。     

Prediction of alternative RNA secondary structures based on fluctuating thermodynamic parameters.

In this paper we present a new method for predicting a set of RNA secondary structures that are thermodynamically favored in RNA folding simulations. This method uses a large number of 'simulated energy rules' (SER) generated by perturbing the free energy parameters derived experimentally within the range of the experimental errors. The structure with the lowest free energy is computed for each SER. Structural comparisons are used to avoid multiple generation of similar structures. Computed structures are evaluated using the energy distribution of the lowest free energy structures derived in the simulation. Predicted be graphically displayed with their occurring frequencies in the simulation by dot-plot representations. On average, about 90% of phylogenetic helixes in the known models of tRNA, Group I self-splicing intron, and Escherichia coli 16 S rRNA, were predicted using the method.     

Higher-order structure of the 5.8 S rRNA sequence within the yeast 35 S precursor ribosomal RNA synthesized in vitro

Dimethylsulfate, 1-cyclohexyl-3-(2-morpholinoethyl)-carbodiimide metho-p-toluene-sulfonate, RNase T1 and RNase V1 have been used as structure-sensitive probes to examine the higher-order structure of the 5.8 S rRNA sequence within the yeast 35 S precursor ribosomal RNA molecule. Data produced have been used to evaluate several theoretical structure models for the 5.8 S rRNA sequence within the precursor rRNA. These models are generated by minimum free energy calculations. A model is proposed that accommodates 83% of the residues experimentally shown to be in either base-paired or single-stranded structure in the correct configuration. Several alternative suboptimal secondary structures have been evaluated. Moreover, the chemical reactivities of several residues within the 5.8 S rRNA sequence in the precursor rRNA molecule differ from those of the corresponding residues in the mature rRNA molecule. This finding provides experimental evidence to support the notion that the 5.8 S rRNA sequence within the precursor rRNA undergoes structural reorganization following rRNA processing.     

Characterization of a novel small RNA encoded by Dictyostelium discoideum mitochondrial DNA

In this study, we analyzed a mitochondrial small (ms) RNA in Dictyostelium discoideum, which is 129 nucleotides long and has a GC content of only 22.5%. In the mitochondrial DNA, a single-copy gene (msr) for the ms RNA was located downstream of the gene for large-subunit rRNA. The location of msr was similar to that of the 5S rRNA gene in prokaryotes and chloroplasts, but clearly different from that in mitochondria of plants, liverwort and the chlorophycean alga Prototheca wikerhamii, in which small-subunit rRNA and 5S rRNA genes are closely linked. The primary sequence of ms RNA showed low homology with mitochondrial 5S rRNA from plants, liverwort and the chlorophycean alga, but the proposed secondary structure of ms RNA was similar to that of cytoplasmic 5S rRNA. In addition, ms RNA showed a highly conserved GAAC sequence in the same loop as in common 5S rRNA. However, ms RNA was detected mainly in the mitochondrial 25?000?×?g supernatant fraction which was devoid of ribosomes. It is possible that ms RNA is an evolutionary derivative of mitochondrial 5S rRNA.     

The nucleotide sequence of 5S rRNA from bovine liver.

We have determined the nucleotide sequence of ribosomal 5S RNA from bovine liver. The comparison of this sequence with those from other eukaryotic sources shows that a common secondary structure model for all eukaryotic 5S rRNAs may exist. Analysis of the evolutionary conserved nucleotides in metazoan 5S rRNAs suggests that the tertiary interactions, proposed earlier for plant 5S rRNA, are also possible.     

Predicting a set of minimal free energy RNA secondary structures common to two sequences

MOTIVATION: Function derives from structure, therefore, there is need for methods to predict functional RNA structures. RESULTS: The Dynalign algorithm, which predicts the lowest free energy secondary structure common to two unaligned RNA sequences, is extended to the prediction of a set of low-energy structures. Dot plots can be drawn to show all base pairs in structures within an energy increment. Dynalign predicts more well-defined structures than structure prediction using a single sequence; in 5S rRNA sequences, the average number of base pairs in structures with energy within 20% of the lowest energy structure is 317 using Dynalign, but 569 using a single sequence. Structure prediction with Dynalign can also be constrained according to experiment or comparative analysis. The accuracy, measured as sensitivity and positive predictive value, of Dynalign is greater than predictions with a single sequence. AVAILABILITY: Dynalign can be downloaded at http://rna.urmc.rochester.edu     

A method for assessing the statistical significance of RNA folding

We have developed a statistical method that is designed for analyzing potential RNA folded substructures. The statistical significance of RNA folding is assessed by the segment score. The segment score is defined as the difference between the lowest free energy calculated for the real biological sequence and the mean of the lowest free energies from random permutations of the real segment sequence, divided by the standard deviation of the random sample. This procedure was applied to the well-studied Escherichia coli 16S rRNA and potato spindle tuber viroid (PSTV) RNA. The results showed that the predictions of the locally significant secondary structures in these two molecules are in accord with the universally conserved local secondary structure elements (Gutell, Weiser & Noller, 1985, Prog. Nucl. Acid Res. molec. Biol. 32, 155-216; Riesner & Gross, 1985, A. Rev. Biochem. 54, 531-564). In addition, a statistical analysis indicated that the lowest free energies of a random sample set follow an approximately normal distribution. A reasonable size for the random sample set was determined statistically. Moreover, the statistical evaluation has been carried out using three different sets of energy rules--two sets (Salser, 1977, Cold Spring Harb. Symp. Quant Biol. 42, 985-1002; Freier, Kierzek, Jaeger, Sugimoto, Caruthers, Neilson & Turner, 1986, Proc. natn. Acad. Sci. U.S.A. 83, 9373-9377) take into account stacking energies and are based on experimental data and their computational extension (Salser, 1977)--the third set is a simplistic "unitary matrix" approach, where any base-pair is given a weight of "minus one" and an unpaired based is "zero". The Freier energy rules usually yield the strongest indication of significant folding region. However, the results derived from paired comparisons test don't provide sufficient evidence for concluding that a different set of energy rules is effective in changing the segment score level for local stem-loop structures in the 16S rRNA.     

Internal transcribed spacer 1 of the yeast precursor ribosomal RNA. Higher order structure and common structural motifs

The higher order structure of the first internal transcribed spacer between the 18S and the 5.8S rRNA sequences in the Saccharomyces cerevisiae precursor ribosomal RNA has been investigated. Sites of potential base pairing in the RNA region have been determined by using a combination of enzymatic and chemical structure sensitive probes. Data generated have been used to evaluate secondary structure models predicted by minimum free energy calculations. Several alternative suboptimal structures were also evaluated. The derived model contains several stable hairpins. Theoretical secondary structural models for the corresponding RNA region from S. carlsbergensis, S. pombe, N. crassa, X. laevis, and mung bean have also been derived from identical calculations and assumptions. Certain structural motifs appear to be conserved despite extensive divergence in the base sequence. The yeast model should be a useful prototype for investigation of structure and function of precursor ribosomal RNA molecules.