The optimality of the standard genetic code assessed by an eight-objective evolutionary algorithm

Abstract

BackgroundThe 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.ResultsTo 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.ConclusionsThe 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.

The influence of different types of translational inaccuracies on the genetic code structure

“…In terms of minimization of amino acid replacements resulting from point mutations in codons, measured here by the average conductance, the SGC is placed between the M 1 codes, characterized by the lowest conductance, and the codes from the M 2 and M 3 models. In agreement with our simulation study, other analyses also showed that the SGC is not perfectly optimized in this respect and better codes can be found [11,44,50,57,58,[87][88][89]. Therefore, it is possible that the assignments of amino acids to codons occurred in accordance with other mechanisms, while the minimization of mutation errors was adjusted by the direct optimization of the mutational pressure around the established genetic code [90][91][92][93][94].…”

“…Moreover, the analysis of the structure and symmetry of the genetic code using binary dichotomy algorithms also showed its immunity to noise in terms of error-detection and error-correction [53][54][55]. The code can be also described as a single-or multi-objective optimization problem using the Evolutionary Algorithms (EA) technique to find optimal genetic codes under various criteria [11,50,[56][57][58]. Such approach revealed that it is possible to find the theoretical codes much better optimized than the SGC.…”

Basic principles of the genetic code extension

“…This approach conforms with the adaptation hypothesis postulating that the standard genetic code evolved to minimize harmful consequences of mutations or mistranslations of coded proteins [Woese, 1965, Sonneborn, 1965, Epstein, 1966, Goldberg and Wittes, 1966]. The SGC turned out to be quite well optimized in this respect when compared with a sample of randomly generated codes [Haig and Hurst, 1991, Freeland and Hurst, 1998a, Freeland and Hurst, 1998b, Freeland et al, 2000, Gilis et al, 2001] but the application of optimization algorithms revealed that the SGC is not perfectly optimized in this respect and more robust codes can be found [Błażej et al, 2018a, Błażej et al, 2016, Massey, 2008, Novozhilov et al, 2007, Santos et al, 2011, Santos and Monteagudo, 2017, Wnetrzak et al, 2018, Błażej et al, 2018b, Błażej et al, 2019b]. The minimization of mutation errors is important from biological point of view, because it protects organism against losing genetic information.…”

“…Then, the reducing of the mutational load seems favoured by biological systems and can occur directly at the level of the mutational pressure [Dudkiewicz et al, 2005, Mackiewicz et al, 2008, Błażej et al, 2013, Błażej et al, 2015, Błażej et al, 2017]. Nevertheless, in the global scale, the SGC shows a general tendency to error minimization [Błażej et al, 2018b, Wnetrzak et al, 2018], which is more exhibited by its alternative versions [Błażej et al, 2019a], evolved later. Therefore, the extension of the SGC according to this rule seems to be a natural consequence of its evolution.…”

Large-scalein silicomutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

“…However, a long and controversial debate regards the level of optimality that SGC has reached. [40,31,39,36,23,38,76].…”