Early Fixation of an Optimal Genetic Code

Abstract

The evolutionary forces that produced the canonical genetic code before the last universal ancestor remain obscure. One hypothesis is that the arrangement of amino acid/codon assignments results from selection to minimize the effects of errors (e.g., mistranslation and mutation) on resulting proteins. If amino acid similarity is measured as polarity, the canonical code does indeed outperform most theoretical alternatives. However, this finding does not hold for other amino acid properties, ignores plausible restrictions on possible code structure, and does not address the naturally occurring nonstandard genetic codes. Finally, other analyses have shown that significantly better code structures are possible. Here, we show that if theoretically possible code structures are limited to reflect plausible biological constraints, and amino acid similarity is quantified using empirical data of substitution frequencies, the canonical code is at or very close to a global optimum for error minimization across plausible parameter space. This result is robust to variation in the methods and assumptions of the analysis. Although significantly better codes do exist under some assumptions, they are extremely rare and thus consistent with reports of an adaptive code: previous analyses which suggest otherwise derive from a misleading metric. However, all extant, naturally occurring, secondarily derived, nonstandard genetic codes do appear less adaptive. The arrangement of amino acid assignments to the codons of the standard genetic code appears to be a direct product of natural selection for a system that minimizes the phenotypic impact of genetic error. Potential criticisms of previous analyses appear to be without substance. That known variants of the standard genetic code appear less adaptive suggests that different evolutionary factors predominated before and after fixation of the canonical code. While the evidence for an adaptive code is clear, the process by which the code achieved this optimization requires further attention.

No accident: genetic codes freeze in error–correcting patterns of the standard genetic code

“…The fraction of permutant codes more physicochemically conservative (as measured by the average absolute difference in physicochemical distance between the amino acids encoded) than the evolved final code along each of the four codon dimensions corresponding to transitions or transversions in the first or second codon position is then recorded. Although Ardell (1998) and Freeland et al (2000) have argued for attention to be paid to the modular power to which distances are raised (using squared or absolute-values distances, for example), what is important for the data we show here is that we are consistent in comparing values that were calculated with the same modular power.…”

“…They proposed that the standard code was selected to reduce the deleterious consequences of errors in the translation (e.g. Woese 1965a;Alff-Steinberger 1969;Swanson 1984;Haig & Hurst 1991;Szathmary & Zintzaras 1992;Goldman 1993;Di Giulio et al 1994;Ardell 1998;Freeland & Hurst 1998;Freeland et al 2000, and others) and hereditary transmission (e.g. Sonneborn 1965;Zuckerkandl & Pauling 1965;Epstein 1966;Goldberg & Wittes 1966;Sitaramam 1989;Joshi et al 1993;Ardell 1998;Sella & Ardell 2002;Ardell & Sella 2001, and others) of proteincoding genes.…”

Study of the genetic code adaptability by means of a genetic algorithm

“…In (Ronneberg et al, 2001) the authors presented a graphical tool for testing the adaptive nature of the genetic code under those assumptions about patterns of genetic error and the nature of amino acid similarity. Additionally, as the adaptability of the genetic code does not hold for other amino acid properties other than polar requirement, in (Freeland et al, 2000b) the authors applied point accepted mutation (PAM) 74-100 matrix data, which derives from frequently observed substitution patterns of amino acids in naturally occurring pairs of homologous proteins. The matrix used by the authors was built solely from evolutionary diverged proteins.…”

Inverse relationship between genetic diversity and epigenetic complexity

“…Numerous studies based on extant organisms have questioned the constancy of mutation rate [5][6][7][8][9][10][11][12][13][14]. The genetic distance between two subpopulations of medaka fish that had diverged for ~ 4 million years is 3-fold greater than that between two different primate species (humans and chimpanzees) that are thought to have diverged for 5-7 million years [15].…”

“…If constant random change within a limited range in genotypes is a hallmark of evolution, then long period of stasis and stability in epigenotypes followed by short period of punctuational advance in epigenotypes is an equally important hallmark of evolution. Indeed, the genetic code is the optimal code for error minimization or for minimizing the effects of random changes; it is the most stable of all possible codes and is optimal for stability rather than for random changes [13].…”