A broader context for understanding amino acid alphabet optimality

Abstract

A series of prior publications has reported unusual properties of the set of genetically encoded amino acids shared by all known life. This work uses quantitative measures (descriptors) of size, charge and hydrophobicity to compare the distribution of the genetically encoded amino acids with random samples of plausible alternatives. Results show that the standard "alphabet" of amino acids established by the time of LUCA is distributed with unusual evenness over a broad range for the three, key physicochemical properties. However, different publications have used slightly different assumptions, including variations in the precise descriptors used, the set of plausible alternative molecules considered, and the format in which results have been presented. Here we consolidate these findings into a unified framework in order to clarify unusual features. We find that in general, the remarkable features of the full set of 20 genetically encoded amino acids are robust when compared with random samples drawn from a densely populated picture of plausible, alternative L-α-amino acids. In particular, the genetically encoded set is distributed across an exceptionally broad range of volumes, and distributed exceptionally evenly within a modest range of hydrophobicities. Surprisingly, range and evenness of charge (pKa) is exceptional only for the full amino acid structures, not for their sidechains - a result inconsistent with prior interpretations involving the role that amino acid sidechains play within protein sequences. In stark contrast, these remarkable features are far less clear when the prebiotically plausible subset of genetically encoded amino acids is compared with a much smaller pool of prebiotically plausible alternatives. By considering the nature of the "optimality theory" approach taken to derive these and prior insights, we suggest productive avenues for further research.

Keywords: Abiogenesis; Amino acids; Computational chemistry; Evolution; Optimality theory; Synthetic biology.