Lessons from an evolving rRNA: 16S and 23S rRNA structures from a comparative perspective (original) (raw)
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Higher-order structure of rRNA
The only reliable general method currently available for determining precise higher order structure in the large ribosomal RNAs is comparative sequence analysis. The method is here applied to reveal 'tertiary' structure in the 16S-like rRNAs, i.e. structure more complex than simple double-helical, secondary structure. From a list of computer-generated potential higher order interactions within 16S rRNA one such interaction considered likely was selected for experimental test. The putative interaction involves a Watson-Crick one to one correspondence between positions 570 and 866 in the molecule (E. coli numbering). Using existing oligonucleotide catalog information several organisms were selected whose 16S rRNA sequences might test the proposed co-variation. In all of the (phylogenetically independent) cases selected, full sequence evidence confirms the predicted one to one (Watson-Crick) correspondence. An interaction between positions 570 and 866 is, therefore, considered proven phylogenetically.
RNA, 1999
Comparative sequence analysis complements experimental methods for the determination of RNA three-dimensional structure. This approach is based on the concept that different sequences within the same gene family form similar higher-order structures. The large number of rRNA sequences with sufficient variation, along with improved covariation algorithms, are providing us with the opportunity to identify new base triples in 16S rRNA. The threedimensional conformations for one of our strongest candidates involving U121 (C124:G237) and/or U121 (U125:A236) (Escherichia coli sequence and numbering) are analyzed here with different molecular modeling tools. Molecular modeling shows that U121 interacts with C124 in the U121 (C124:G237) base triple. This arrangement maintains isomorphic structures for the three most frequent sequence motifs (approximately 93% of known bacterial and archaeal sequences), is consistent with chemical reactivity of U121 in E. coli ribosomes, and is geometrically favorable. Further, the restricted set of observed canonical (GU, AU, GC) base-pair types at positions 124:237 and 125:236 is consistent with the fact that the canonical base-pair sets (for both base pairs) that are not observed in nature prevent the formation of the 121(124:237) base triple. The analysis described here serves as a general scheme for the prediction of specific secondary and tertiary structure base pairing where there is a network of correlated base changes.
Prediction of secondary structures of 16S and 23S rRNA fromEscherichia coli
Journal of Biosciences, 1985
Small and large subunits of Escherichia coli ribosome have three different rRNAs, the sequences of which are known. However, attempts by three groups to predict secondary structures of 16S and 23S rRNAs have certain common limitations namely, these structures are predicted assuming no interactions among various domains of the molecule and only 40% residues are involved in base pairing as against the experimental observation of 60 % residues in base paired state. Recent experimental studies have shown that there is a specific interaction between naked 16S and 23S rRNA molecules. This is significant because we have observed that the regions (oligonucleotides of length 9-10 residues), in 16S rRNA which are complementary to those in 23S rRNA do not have internal complementary sequences. Therefore, we have developed a simple graph theoretical approach to predict secondary structures of 16S and 23S rRNAs. Our method for model building not only uses complete sequence of 16S or 23S rRNA molecule along with other experimental observations but also takes into account the observation that specific recognition is possible through the complementary sequences between 16S and 23S rRNA molecules and, therefore, these parts of the molecules are not used for internal base pairing. The method used to predict secondary structures is discussed. A typical secondary structure of the complex between 16S and 23S rRNA molecules, obtained using our method, is presented and compared briefly with earlier model building studies.
Evolution of tRNA into rRNA secondary structures
Gene Reports, 2019
RNA and protein structures enable reconstructing ancient evolution because: 1. structures evolve more slowly than primary sequences; 2. Ancient self-organized patterns reappear spontaneously and/or 3. re-evolve secondarily. Previous analyses grouped RNA secondary structures in (a) presumably primitive, short RNAs rich in external loops (topping stems) and few bulges (unpaired nucleotides within stems); and (b) longer, more derived RNAs with more bulges, presumed regulatory endonuclease targets. These represent the main axis of RNA evolution from (a) tRNA-like to (b) rRNA-like. We suggest that relative similarity of tRNAs to (a) reflects antiquity, and to (b) the opposite, predicting that tRNA scores on this tRNA-rRNA axis converge with genetic code inclusion orders of tRNA cognate amino acids. This occurs in particular according to amino acids ranked inversely to tRNA isoacceptor diversity, and mainly in evolutionarily ancient organisms. Putatively, in some organisms, tRNA cloverleaves sometimes recover convergence with genetic code inclusion orders of cognate amino acids, probably because original ancient evolutionary processes integrated functional constraints, i.e., tRNA distinguishability to avoid misacylations. Results confirm the direction, evolutionary and biological relevance of the tRNA-rRNA secondary structure axis as RNA's major evolutionary axis, a potential calibrator of biomolecular evolution. adaptive components integral to the original evolving self-organizing system, notably self-correction properties (Seligmann, 2018a). 1.1. Genetic code inclusion order of amino acid-codon assignments A large number of hypotheses predict the order of amino acid integration in the genetic code, meaning their assignment to codons. These follow diverse approaches, from physicochemical properties of amino acids, i.e., their chemical inertness (Trifonov, 1999), structural complexity (Dufton, 1997; Trifonov and Bettecken, 1997) and stereochemical affinities with nucleotide triplets (Yarus and Christian, 1989; Yarus et al., 2009; Yarus, 2017), including differences between Land D-amino acid enantiomers and their preferences for interacting with D-RNA (Root-Bernstein, 2007; Han et al., 2010; Michel and Seligmann, 2014), to coevolution between metabolic pathways, for example Nfixation (Davis, 1999) and parallels between metabolism of amino acids and their codon assignment (Wong, 1975, 2005). Many hypotheses produce similar/congruent genetic code integration orders for amino acids (Trifonov, 2000, 2004), but it is plausible that different components of the complex translation machinery whose sum produces the genetic code had at least partly independent
Secondary structure and domain architecture of the 23S and 5S rRNAs
Nucleic Acids Research, 2013
We present a de novo re-determination of the secondary (2 ) structure and domain architecture of the 23S and 5S rRNAs, using 3D structures, determined by X-ray diffraction, as input. In the traditional 2 structure, the center of the 23S rRNA is an extended single strand, which in 3D is seen to be compact and double helical. Accurately assigning nucleotides to helices compels a revision of the 23S rRNA 2 structure. Unlike the traditional 2 structure, the revised 2 structure of the 23S rRNA shows architectural similarity with the 16S rRNA. The revised 2 structure also reveals a clear relationship with the 3D structure and is generalizable to rRNAs of other species from all three domains of life. The 2 structure revision required us to reconsider the domain architecture. We partitioned the 23S rRNA into domains through analysis of molecular interactions, calculations of 2D folding propensities and compactness. The best domain model for the 23S rRNA contains seven domains, not six as previously ascribed. Domain 0 forms the core of the 23S rRNA, to which the other six domains are rooted. Editable 2 structures mapped with various data are provided (http://apollo.chemistry.gatech.edu/RibosomeGallery).
Improved statistical methods reveal direct interactions between 16S and 23S rRNA
Nucleic Acids Research, 2000
Recent biochemical studies have indicated a number of regions in both the 16S and 23S rRNA that are exposed on the ribosomal subunit surface. In order to predict potential interactions between these regions we applied novel phylogenetically-based statistical methods to detect correlated nucleotide changes occurring between the rRNA molecules. With these methods we discovered a number of highly significant correlated changes between different sets of nucleotides in the two ribosomal subunits. The predictions with the highest correlation values belong to regions of the rRNA subunits that are in close proximity according to recent crystal structures of the entire ribosome. We also applied a new statistical method of detecting base triple interactions within these same rRNA subunit regions. This base triple statistic predicted a number of new base triples not detected by pair-wise interaction statistics within the rRNA molecules. Our results suggest that these statistical methods may enhance the ability to detect novel structural elements both within and between RNA molecules.
Nucleic Acids Research, 1981
We determined 90% of the primary structure of E.coli MRE 600 23S rRNA by applying the sequencing gel technique to products of Tl, SI, A and Naja oxiana nuclease digestion. Eight cistron heterogeneities were detected, as well as 16 differences with the published sequence of a 23S rRNA gene of an E.coli K12 strain. The positions of 13 post-transcriptionally modified nucleotides and of single-stranded, double-stranded and subunit surface regions of E.coli 23S rRNA were identified. Using these experimental results and by comparing the sequences of E.coli 23S rRNA, maize chloro. 23S rRNA and mouse and human mit 16S rRNAs, we built models of secondary structure for the two 23S rRNAs and for large portions of the two mit rRNAs. The structures proposed for maize chloroplast and E.coli 23S rRNAs are very similar, consisting of 7 domains closed by long-range base-pairings. In the mitochondrial 16S rRNAs, 3 of these domains are strongly reduced in size and have a very different primary structure compared to those of the 23S rRNAs. These domains were previously found to constitute a compact area in the E.coli 50S subunits. The conserved domains do not belong to this area and contain almost all the modified nucleotides. The most highly conserved domain, 2042-2625, is probably part of the ribosomal A site. Finally, our study strongly suggests that in cytoplasmic ribosomes the 3'-end of 5.8S rRNA is basepaired with the 5'-end of 26S or 28S rRNA. This confirms the idea that 5.8S RNA is the counterpart of the 5'-terminal region of prokaryotic 23S rRNA.
The origin of modern 5S rRNA: a case of relating models of structural history to phylogenetic data
2010
Evolutionary models of molecular structures must incorporate molecular information at different levels of structural complexity and must be phrased within a phylogenetic perspective. In this regard, phylogenetic trees of substructures that are reconstructed from molecular features that contribute to order and thermodynamic stability show that a gradual model of evolution of 5S rRNA structure is more parsimonious than models that invoke large segmental duplications of the molecule. The search for trees of substructures that are most parsimonious, by their very nature, defines an objective strategy to select models of molecular change that best fit structural data. When combined with additional data, such as the age of protein domains that interact with RNA substructures, these trees can be used to falsify unlikely hypotheses.