Login / Signup

Common evolutionary origins of the bacterial glycyl tRNA synthetase and alanyl tRNA synthetase.

Claudia Alvarez-CarreñoMarcelino ArciniegaLluis Ribas de PouplanaAnton S PetrovAdriana Hernández-GonzálezMarco Igor Valencia-SánchezLoren Dean WilliamsAlfredo Torres-Larios
Published in: Protein science : a publication of the Protein Society (2023)
Aminoacyl-tRNA synthetases (aaRSs) establish the genetic code. Each aaRS covalently links a given canonical amino acid to a cognate set of tRNA isoacceptors. Glycyl tRNA aminoacylation is unusual in that it is catalyzed by different aaRSs in different lineages of the Tree of Life. We have investigated the phylogenetic distribution and evolutionary history of bacterial glycyl tRNA synthetase (bacGlyRS). This enzyme is found in early diverging bacterial phyla such as Firmicutes, Acidobacteria, and Proteobacteria, but not in archaea or eukarya. We observe relationships between each of six domains of bacGlyRS and six domains of four different RNA-modifying proteins. Component domains of bacGlyRS show common ancestry with i) the catalytic domain of class II tRNA synthetases; ii) the HD domain of the bacterial RNase Y; iii) the body and tail domains of the archaeal CCA-adding enzyme; iv) the anti-codon binding domain of the arginyl tRNA synthetase; and v) a previously unrecognized domain that we call ATL (Ancient tRNA latch). The ATL domain is found only in bacGlyRS and in the universal alanyl tRNA synthetase (uniAlaRS). Further, the catalytic domain of bacGlyRS is more closely related to the catalytic domain of uniAlaRS than to any other aminoacyl tRNA synthetase. The combined data suggest that the ATL and catalytic domains of these two enzymes are ancestral to bacGlyRS and uniAlaRS, which emerged from common protein ancestors by bricolage, stepwise accumulation of protein domains, before the last universal common ancestor of life. This article is protected by copyright. All rights reserved.
Keyphrases
  • amino acid
  • machine learning
  • oxidative stress
  • deep learning
  • high resolution
  • genome wide association study