Genetic Code Error Minimization as a Non-Adaptive But Beneficial Trait

Massey, Steven E.

doi:10.1007/s00239-018-9880-6

Cited by 10 publications

(18 citation statements)

References 25 publications

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“…A third neutral mechanism has been tested (Massey 2008(Massey , 2016(Massey , 2019. Because successor RNA-amino acid interactions would likely be molecular derivatives of prior RNA-amino acid interactions, they would employ related sequences.…”

Section: Neutral Change: Paralogymentioning

confidence: 99%

Evolution of the Standard Genetic Code

Yarus

2021

J Mol Evol

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A near-universal Standard Genetic Code (SGC) implies a single origin for present Earth life. To study this unique event, I compute paths to the SGC, comparing different plausible histories. Notably, SGC-like coding emerges from traditional evolutionary mechanisms, and a superior route can be identified. To objectively measure evolution, progress values from 0 (random coding) to 1 (SGC-like) are defined: these measure fractions of random-code-to-SGC distance. Progress types are spacing/distance/delta Polar Requirement, detecting space between identical assignments/mutational distance to the SGC/chemical order, respectively. The coding system is based on selected RNAs performing aminoacyl-RNA synthetase reactions. Acceptor RNAs exhibit SGC-like Crick wobble; alternatively, non-wobbling triplets uniquely encode 20 amino acids/start/stop. Triplets acquire 22 functions by stereochemistry, selection, coevolution, or at random. Assignments also propagate to an assigned triplet’s neighborhood via single mutations, but can also decay. A vast code universe makes futile evolutionary paths plentiful. Thus, SGC evolution is critically sensitive to disorder from random assignments. Evolution also inevitably slows near coding completion. The SGC likely avoided these difficulties, and two suitable paths are compared. In late wobble, a majority of non-wobble assignments are made before wobble is adopted. In continuous wobble, a uniquely advantageous early intermediate yields an ordered SGC. Revised coding evolution (limited randomness, late wobble, concentration on amino acid encoding, chemically conservative coevolution with a chemically ordered elite) produces varied full codes with excellent joint progress values. A population of only 600 independent coding tables includes SGC-like members; a Bayesian path toward more accurate SGC evolution is available.

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Section: Neutral Change: Paralogymentioning

confidence: 99%

Evolution of the Standard Genetic Code

Yarus

2021

J Mol Evol

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“…Fraction of coding tables with coding capacity (≥ 20 encoded functions: left ordinate) or SGClike assignments (0 mis, no misassignment; ≤ 1 mis, ≤ 1 misassignment; ≤ 2 mis, ≤ 2 misassignments: right ordinate), versus evolutionary time as passages. 100,000 late wobble evolutions via Coevo_PR captures: coevolution (Wong 1981;Amirnovin 1997;Ronneberg et al 2000) with chemical similarity (Freeland and Hurst 1998;Massey 2019) to direct amino acid choice. Probabilities per passage are Pmut = 0.04 for mutational capture, Pdecay = 0.04 for assignment loss, Pinit = 0.6 for initial triplet assignment, Prand = 0.1 for random assignment (Yarus 2020).…”

Section: Filling the Coding Tablementioning

confidence: 99%

Optimal Evolution of the Standard Genetic Code

Yarus¹

2020

Preprint

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The Standard Genetic Code (SGC) exists in every organism known on Earth. SGC evolution via early unique codon assignment, then later wobble, yields coding resembling the near-universal code. Below, later wobble also creates an optimal route to accurate codon assignment. This assignment time matches a previous mean time for ordered codes, exhibiting ≥ 90% of SGC order. Accurate evolution is also accessible, sufficiently frequent to appear in populations of 103 to 104 codes. SGC-like coding capacity, code order and assignments therefore arise together, in one attainable evolutionary intermediate. Examples, which plausibly resemble coding at evolutionary domain separation, are characterized.

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“…In conclusion, the neutral hypothesis of the origin of error minimization (Massey, 2008(Massey, , 2016(Massey, , 2018(Massey, , 2019Koonin, 2017) should have been falsified under the hypothesis that the genetic code was structured using the mechanism envisaged by the coevolution theory. Koonin (2017) also argues that error minimization, despite being a key property of the genetic code, "likely evolved as a by-product of code expansion rather than by direct selection for code robustness" because "the coevolution model, although producing some level of error minimization, was substantially inferior to the other scenarios " (Koonin, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Given that a model has been investigated here that combines two factors believed to be responsible for the origin of the genetic code -that is to say, the biosynthetic relationships between amino acids and their physicochemical properties (Di Giulio, 2013) -we are in a position to respond to this question at least under these conditions. Indeed, the mechanism on which the coevolution theory is based (Wong, 1975;Wachtershauser, 1988;Danchin, 1989;de Duve, 1991;Edwards, 1996;Di Giulio, 1997b, 2008a could actually have been the mechanism that was structuring the genetic code (Di Giulio, 2018a) and therefore -in accordance with the neutral hypothesis (Massey, 2008(Massey, , 2016(Massey, , 2018(Massey, , 2019 -might have given rise to the minimization of the error as its by-product, without direct action of natural selection (Massey, 2008(Massey, , 2016. It would seem self-evident that the levels of optimization reached by the genetic code with the relative probabilities (Tabs.…”

Section: Discussionmentioning

confidence: 99%

“…In conclusion, we must believe that the extraordinary property of error minimization possessed by the genetic code did not emerge as a consequence of a simple neutral process -as claimed by Massey (2008Massey ( , 2016Massey ( , 2018Massey ( , 2019 and Koonin (2017) -but is instead a clear manifestation of the strength of natural selection (Di Giulio, 2018a).…”

Section: Discussionmentioning

confidence: 99%

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The genetic code is very close to a global optimum in a model of its origin taking into account both the partition energy of amino acids and their biosynthetic relationships

Giulio¹,

Caldararo²

2021

Preprint

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We used the Moran's I index of global spatial autocorrelation with the aim of studying the distribution of the physicochemical or biological properties of amino acids within the genetic code table. First, using this index we are able to identify the amino acid property - among the 530 analyzed - that best correlates with the organization of the genetic code in the set of amino acid permutation codes. Considering, then, a model suggested by the coevolution theory of the genetic code origin - which in addition to the biosynthetic relationships between amino acids took into account also their physicochemical properties - we investigated the level of optimization achieved by these properties either on the entire genetic code table, or only on its columns or only on its rows. Specifically, we estimated the optimization achieved in the restricted set of amino acid permutation codes subject to the constraints derived from the biosynthetic classes of amino acids, in which we identify the most optimized amino acid property among all those present in the database. Unlike what has been claimed in the literature, it would appear that it was not the polarity of amino acids that structured the genetic code, but that it could have been their partition energy instead. In actual fact, it would seem to reach an optimization level of about 96% on the whole table of the genetic code and 98% on its columns. Given that this result has been obtained for amino acid permutation codes subject to biosynthetic constraints, that is to say, for a model of the genetic code consistent with the coevolution theory, we should consider the following conclusions reasonable. (i) The coevolution theory might be corroborated by these observations because the model used referred to the biosynthetic relationships between amino acids, which are suggested by this theory as having been fundamental in structuring the genetic code. (ii) The very high optimization on the columns of the genetic code would not only be compatible but would further corroborate the coevolution theory because this suggests that, as the genetic code was structured along its rows by the biosynthetic relationships of amino acids, on its columns strong selective pressure might have been put in place to minimize, for example, the deleterious effects of translation errors. (iii) The finding that partition energy could be the most optimized property of amino acids in the genetic code would in turn be consistent with one of the main predictions of the coevolution theory. In other words, since the partition energy is reflective of the protein structure and therefore of the enzymatic catalysis, the latter might really have been the main selective pressure that would have promoted the origin of the genetic code. Indeed, we observe that the beta-strands show an optimization percentage of 94.45%, so it is possible to hypothesize that they might have become the object of selection during the origin of the genetic code, conditioning the choice of biosynthetic relationships between amino acids. (iv) The finding that the polarity of amino acids is less optimized than their partition energy in the genetic code table might be interpreted against the physicochemical theories of the origin of the genetic code because these would suggest, for example, that a very high optimization of the polarity of amino acids in the code could be an expression of interactions between amino acids and codons or anticodons, which would have promoted their origin. This might now become less sustainable, given the very high optimization that is instead observed in favor of partition energy but not polarity. Finally, (v) the very high optimization of the partition energy of amino acids would seem to make a neutral origin of the ability of the genetic code to buffer, for example, the deleterious effects of translation errors very unlikely. Indeed, an optimization of about 100% would seem that it might not have been achieved by a simple neutral process, but this ability should probably have been generated instead by the intervention of natural selection. In actual fact, we show that the neutral hypothesis of the origin of error minimization has been falsified for the model analyzed here. Therefore, we will discuss our observations within the theories proposed to explain the origin of the organization of the genetic code, reaching the conclusion that the coevolution theory is the most strongly corroborated theory.

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Genetic Code Error Minimization as a Non-Adaptive But Beneficial Trait

Cited by 10 publications

References 25 publications

Evolution of the Standard Genetic Code

Evolution of the Standard Genetic Code

Optimal Evolution of the Standard Genetic Code

The genetic code is very close to a global optimum in a model of its origin taking into account both the partition energy of amino acids and their biosynthetic relationships

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