Nucleic Acids Research Advance Access published online on August 17, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm598
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In silico detection of tRNA sequence features characteristic to aminoacyl-tRNA synthetase class membership
1Theoretical Biology and Ecology Research Group of the Hungarian Academy of Sciences, Department of Plant Taxonomy and Ecology, 2eScience Regional Knowledge Center, at Eötvös Loránd University, 3Collegium Budapest, Institute for Advanced Study, Budapest, Hungary, 4Department of Biochemistry and 5Department of Plant Taxonomy and Ecology, Eötvös Loránd University, Budapest, Hungary
*To whom correspondence should be addressed. Tel: +36 1 2090555/8577; Fax: +36 1 3812172; Email: palgabor{at}elte.hu
Received December 18, 2006. Revised July 6, 2007. Accepted July 17, 2007.
Aminoacyl tRNA synthetases (aaRS) are grouped into Class I and II based on primary and tertiary structure and enzyme properties suggesting two independent phylogenetic lineages. Analogously, tRNA molecules can also form two respective classes, based on the class membership of their corresponding aaRS. Although some aaRS–tRNA interactions are not extremely specific and require editing mechanisms to avoid misaminoacylation, most aaRS–tRNA interactions are rather stereospecific. Thus, class-specific aaRS features could be mirrored by class-specific tRNA features. However, previous investigations failed to detect conserved class-specific nucleotides. Here we introduce a discrete mathematical approach that evaluates not only class-specific ‘strictly present’, but also ‘strictly absent’ nucleotides. The disjoint subsets of these elements compose a unique partition, named extended consensus partition (ECP). By analyzing the ECP for both Class I and II tDNA sets from 50 (13 archaeal, 30 bacterial and 7 eukaryotic) species, we could demonstrate that class-specific tRNA sequence features do exist, although not in terms of strictly conserved nucleotides as it had previously been anticipated. This finding demonstrates that important information was hidden in tRNA sequences inaccessible for traditional statistical methods. The ECP analysis might contribute to the understanding of tRNA evolution and could enrich the sequence analysis tool repertoire.