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Little Evidence the Standard Genetic Code Is Optimized for Resource Conservation.

ABSTRACT: Selection for resource conservation can shape the coding sequences of organisms living in nutrient-limited environments. Recently, it was proposed that selection for resource conservation, specifically for nitrogen and carbon content, has also shaped the structure of the standard genetic code, such that the missense mutations the code allows tend to cause small increases in the number of nitrogen and carbon atoms in amino acids. Moreover, it was proposed that this optimization is not confounded by known optimizations of the standard genetic code, such as for polar requirement or hydropathy. We challenge these claims. We show the proposed optimization for nitrogen conservation is highly sensitive to choice of null model and the proposed optimization for carbon conservation is confounded by the known conservative nature of the standard genetic code with respect to the molecular volume of amino acids. There is therefore little evidence the standard genetic code is optimized for resource conservation. We discuss our findings in the context of null models of the standard genetic code.

SUBMITTER: Rozhonova H

PROVIDER: S-EPMC8557452 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Little Evidence the Standard Genetic Code Is Optimized for Resource Conservation.

Rozhoňová Hana H Payne Joshua L JL

Molecular biology and evolution 20211001 11

Selection for resource conservation can shape the coding sequences of organisms living in nutrient-limited environments. Recently, it was proposed that selection for resource conservation, specifically for nitrogen and carbon content, has also shaped the structure of the standard genetic code, such that the missense mutations the code allows tend to cause small increases in the number of nitrogen and carbon atoms in amino acids. Moreover, it was proposed that this optimization is not confounded ...[more]

PMID: 34373928

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Project description:BACKGROUND:The standard genetic code (SGC) is a unique set of rules which assign amino acids to codons. Similar amino acids tend to have similar codons indicating that the code evolved to minimize the costs of amino acid replacements in proteins, caused by mutations or translational errors. However, if such optimization in fact occurred, many different properties of amino acids must have been taken into account during the code evolution. Therefore, this problem can be reformulated as a multi-objective optimization task, in which the selection constraints are represented by measures based on various amino acid properties. RESULTS:To study the optimality of the SGC we applied a multi-objective evolutionary algorithm and we used the representatives of eight clusters, which grouped over 500 indices describing various physicochemical properties of amino acids. Thanks to that we avoided an arbitrary choice of amino acid features as optimization criteria. As a consequence, we were able to conduct a more general study on the properties of the SGC than the ones presented so far in other papers on this topic. We considered two models of the genetic code, one preserving the characteristic codon blocks structure of the SGC and the other without this restriction. The results revealed that the SGC could be significantly improved in terms of error minimization, hereby it is not fully optimized. Its structure differs significantly from the structure of the codes optimized to minimize the costs of amino acid replacements. On the other hand, using newly defined quality measures that placed the SGC in the global space of theoretical genetic codes, we showed that the SGC is definitely closer to the codes that minimize the costs of amino acids replacements than those maximizing them. CONCLUSIONS:The standard genetic code represents most likely only partially optimized systems, which emerged under the influence of many different factors. Our findings can be useful to researchers involved in modifying the genetic code of the living organisms and designing artificial ones.

Project description:BackgroundThe arrangement of the amino acids in the genetic code is such that neighbouring codons are assigned to amino acids with similar physical properties. Hence, the effects of translational error are minimized with respect to randomly reshuffled codes. Further inspection reveals that it is amino acids in the same column of the code (i.e. same second base) that are similar, whereas those in the same row show no particular similarity. We propose a 'four-column' theory for the origin of the code that explains how the action of selection during the build-up of the code leads to a final code that has the observed properties.ResultsThe theory makes the following propositions. (i) The earliest amino acids in the code were those that are easiest to synthesize non-biologically, namely Gly, Ala, Asp, Glu and Val. (ii) These amino acids are assigned to codons with G at first position. Therefore the first code may have used only these codons. (iii) The code rapidly developed into a four-column code where all codons in the same column coded for the same amino acid: NUN = Val, NCN = Ala, NAN = Asp and/or Glu, and NGN = Gly. (iv) Later amino acids were added sequentially to the code by a process of subdivision of codon blocks in which a subset of the codons assigned to an early amino acid were reassigned to a later amino acid. (v) Later amino acids were added into positions formerly occupied by amino acids with similar properties because this can occur with minimal disruption to the proteins already encoded by the earlier code. As a result, the properties of the amino acids in the final code retain a four-column pattern that is a relic of the earliest stages of code evolution.ConclusionThe driving force during this process is not the minimization of translational error, but positive selection for the increased diversity and functionality of the proteins that can be made with a larger amino acid alphabet. Nevertheless, the code that results is one in which translational error is minimized. We define a cost function with which we can compare the fitness of codes with varying numbers of amino acids, and a barrier function, which measures the change in cost immediately after addition of a new amino acid. We show that the barrier is positive if an amino acid is added into a column with dissimilar properties, but negative if an amino acid is added into a column with similar physical properties. Thus, natural selection favours the assignment of amino acids to the positions that they occupy in the final code.

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Little Evidence the Standard Genetic Code Is Optimized for Resource Conservation.

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Little Evidence the Standard Genetic Code Is Optimized for Resource Conservation.

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