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The ancient operational code is embedded in the amino acid substitution matrix and aaRS phylogenies

journal contribution
posted on 2023-05-20, 19:26 authored by Julia Shore, Barbara HollandBarbara Holland, Jeremy SumnerJeremy Sumner, Nieselt, K, Wills, PR
The underlying structure of the canonical amino acid substitution matrix (aaSM) is examined by considering stepwise improvements in the differential recognition of amino acids according to their chemical properties during the branching history of the two aminoacyl-tRNA synthetase (aaRS) superfamilies. The evolutionary expansion of the genetic code is described by a simple parameterization of the aaSM, in which (i) the number of distinguishable amino acid types, (ii) the matrix dimension and (iii) the number of parameters, each increases by one for each bifurcation in an aaRS phylogeny. Parameterized matrices corresponding to trees in which the size of an amino acid sidechain is the only discernible property behind its categorization as a substrate, exclusively for a Class I or II aaRS, provide a significantly better fit to empirically determined aaSM than trees with random bifurcation patterns. A second split between polar and nonpolar amino acids in each Class effects a vastly greater further improvement. The earliest Class-separated epochs in the phylogenies of the aaRS reflect these enzymes’ capability to distinguish tRNAs through the recognition of acceptor stem identity elements via the minor (Class I) and major (Class II) helical grooves, which is how the ancient operational code functioned. The advent of tRNA recognition using the anticodon loop supports the evolution of the optimal map of amino acid chemistry found in the later genetic code, an essentially digital categorization, in which polarity is the major functional property, compensating for the unrefined, haphazard differentiation of amino acids achieved by the operational code.

Funding

Australian Research Council

History

Publication title

Journal of Molecular Evolution

Volume

88

Pagination

136-150

ISSN

0022-2844

Department/School

School of Natural Sciences

Publisher

Springer-Verlag

Place of publication

175 Fifth Ave, New York, USA, Ny, 10010

Rights statement

Copyright 2019 Springer Science+Business Media, LLC, part of Springer Nature

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the biological sciences; Expanding knowledge in the mathematical sciences