In silico detection of tRNA sequence features characteristic to aminoacyl-tRNA synthetase class membership

• Published in: Nucleic acids research Vol. 35; no. 16; pp. 5593 - 5609
• Main Authors: Jakó, Eena, Ittzés, Péter, Szenes, Aron, Kun, Adám, Szathmáry, Eörs, Pál, Gábor
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-08-2007; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino Acyl-tRNA Synthetases - classification; Animals; Computational Biology; Humans; RNA; RNA, Archaeal - genetics; RNA, Bacterial - genetics; RNA, Fungal - genetics; RNA, Transfer - classification; RNA, Transfer - genetics; Saccharomyces cerevisiae - genetics; Sequence Alignment; Sequence Analysis, DNA - methods
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkm598

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.