Genetic Code Evolution Reveals the Neutral Emergence of Mutational Robustness, and Information as an Evolutionary Constraint

Abstract

The standard genetic code (SGC) is central to molecular biology and its origin and evolution is a fundamental problem in evolutionary biology, the elucidation of which promises to reveal much about the origins of life. In addition, we propose that study of its origin can also reveal some fundamental and generalizable insights into mechanisms of molecular evolution, utilizing concepts from complexity theory. The first is that beneficial traits may arise by non-adaptive processes, via a process of “neutral emergence”. The structure of the SGC is optimized for the property of error minimization, which reduces the deleterious impact of point mutations. Via simulation, it can be shown that genetic codes with error minimization superior to the SGC can emerge in a neutral fashion simply by a process of genetic code expansion via tRNA and aminoacyl-tRNA synthetase duplication, whereby similar amino acids are added to codons related to that of the parent amino acid. This process of neutral emergence has implications beyond that of the genetic code, as it suggests that not all beneficial traits have arisen by the direct action of natural selection; we term these “pseudaptations”, and discuss a range of potential examples. Secondly, consideration of genetic code deviations (codon reassignments) reveals that these are mostly associated with a reduction in proteome size. This code malleability implies the existence of a proteomic constraint on the genetic code, proportional to the size of the proteome (P), and that its reduction in size leads to an “unfreezing” of the codon – amino acid mapping that defines the genetic code, consistent with Crick’s Frozen Accident theory. The concept of a proteomic constraint may be extended to propose a general informational constraint on genetic fidelity, which may be used to explain variously, differences in mutation rates in genomes with differing proteome sizes, differences in DNA repair capacity and genome GC content between organisms, a selective pressure in the evolution of sexual reproduction, and differences in translational fidelity. Lastly, the utility of the concept of an informational constraint to other diverse fields of research is explored.

The information capacity of the genetic code: Is the natural code optimal?

“…The problem with such work is that substitution matrices such as PAM are employed which leads to a tautology (and the claim of the optimality of the natural code) since these matrices are the result of the natural genetic code (Di Giulio, 2001). For other references on the topic the reader is referred to Massey (2015).…”

The structure of the genetic code as an optimal graph clustering problem

“…Consequently, the potential tendencies of this code 435 to minimize the errors may be a by-product of this process [Di Giulio, 2016, Di Giulio, 436 2017. Other studies have also showed that no direct selection for the error minimization 437 was necessary to produce the genetic codes with this property, which could have evolved 438 as a result of gene duplications of adaptors and charging enzymes [Massey, 2015, Massey, 439 2016. Interestingly, the optimization of biological systems to minimize the harmful 440 effects of mutations does not have to require changes in the genetic code because the 441 mutational pressure can be subjected to this optimization around the fixed genetic 442 code [Dudkiewicz et al, 2005, Mackiewicz et al, 2008, B lażej et al, 2015 2017].…”

Frozen Accident Pushing 50: Stereochemistry, Expansion and Chance in the Evolution of the Genetic Code

“…Hence the error minimization theory of code evolution, under which the structure of the code is determined by selection for robustness to errors. Extensive quantitative analyses that employed cost functions differently derived from physico-chemical properties of amino acids have shown that the code is indeed highly resilient, with the probability to pick an equally robust random code being on the order of 10 -7 -10 -8 [14,15,[61][62][63][64][65][66][67][68]. Obviously, however, among the ~10 84 possible random codes, there is a huge number with a higher degree of error minimization than the SGC.…”

“…Given the confidently derived consensus set of ancient amino acids and the unavailability of the late amino acids before complex biosynthetic pathways evolved (see discussion of The theme of evolution of error minimization as a neutral consequence, or by-product, of the code expansion has been thoroughly developed in several recent studies [64,67,68].…”

“…A more biologically sound approach involves reconstruction of the routes of code expansion that might produce error minimization as a selectively neutral byproduct of evolution driven by other factors [64,67,68].…”