The Genetic Code Is One in a Million

@article{Freeland1998TheGC,
  title={The Genetic Code Is One in a Million},
  author={Stephen J. Freeland and Laurence D. Hurst},
  journal={Journal of Molecular Evolution},
  year={1998},
  volume={47},
  pages={238-248}
}
Abstract. [] Key Result We find that if the bias affects all codon positions equally, as might be expected were the code adapted to a mutational environment with transition/transversion bias, then any reasonable transition/transversion bias increases the relative efficiency of the second base by an order of magnitude. In addition, if we employ weightings to allow for biases in translation, then only 1 in every million random alternative codes generated is more efficient than the natural code. We thus…

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The model establishes a weighting scheme for mistranslation biases of the three different codon positions, transition/transversion biases, and codon usage that shows that the natural genetic code is not fully optimized for error minimization.

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Early fixation of an optimal genetic code.

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Load minimization of the genetic code: history does not explain the pattern

  • S. FreelandL. Hurst
  • Biology
    Proceedings of the Royal Society of London. Series B: Biological Sciences
  • 1998
The error–minimizing ability of the natural code is compared with that of alternative codes which are restricted such that amino acids from the same biochemical pathway all share the same first base, and it is found that although on average the restricted set of codes show a slightly higher efficiency than random ones, the real code remains extremely efficient relative to this subset p=0.3.

Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm

This work investigated the robustness of this code regarding the cost of amino acid replacements resulting from substitutions in these positions separately and the sum of these costs and suggested that the optimality of SGC could be a by-product of other processes.

An Alternative Look at Code Evolution: Using Non-canonical Codes to Evaluate Adaptive and Historic Models for the Origin of the Genetic Code

The levels of robustness in existing non-canonical codes as well as codes that differ in only one codon assignment from the standard code are evaluated and it is found that many of these codes is comparable or better than that of thestandard code.
...

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