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

Małgorzata Wnętrzak1, Paweł Błażej1, Dorota Mackiewicz1

  • 1Department of Genomics, Faculty of Biotechnology, University of Wrocław, ul. Joliot-Curie 14a, 50-383, Wrocław, Poland.

BMC Evolutionary Biology
|December 15, 2018
PubMed
Summary

The standard genetic code is not fully optimized for minimizing errors, though it favors amino acid replacements that reduce costs. This study used evolutionary algorithms to analyze code optimality.

Keywords:
EvolutionGenetic algorithmMutationOptimizationStandard genetic codeTranslation

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Area of Science:

  • Biochemistry
  • Evolutionary Biology
  • Bioinformatics

Background:

  • The standard genetic code (SGC) assigns amino acids to codons, with similar amino acids often having similar codons.
  • This arrangement suggests evolution favored minimizing costs from mutations and translational errors.
  • However, optimizing the code likely involved numerous amino acid properties, posing a multi-objective optimization challenge.

Purpose of the Study:

  • To investigate the optimality of the standard genetic code (SGC) regarding error minimization.
  • To compare the SGC's structure against theoretically optimized genetic codes.
  • To assess the SGC's position relative to codes minimizing or maximizing amino acid replacement costs.

Main Methods:

  • Applied a multi-objective evolutionary algorithm to analyze the SGC.
  • Utilized representatives from eight clusters encompassing over 500 physicochemical properties of amino acids.
  • Modeled the genetic code with and without the characteristic codon blocks structure.

Main Results:

  • The SGC is not fully optimized for error minimization and can be significantly improved.
  • The SGC's structure differs notably from codes optimized for minimizing amino acid replacement costs.
  • The SGC is closer to codes that minimize replacement costs than those that maximize them.

Conclusions:

  • The standard genetic code is likely a partially optimized system shaped by multiple evolutionary factors.
  • Findings offer insights for researchers modifying or designing genetic codes for organisms.