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. 2018 Feb 23;8:3532. doi: 10.1038/s41598-018-21973-y

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Regularities of the artificial genetic codes. We analysed the average properties of the 50 most optimal artificial genetic codes, one from each of 50 the independent runs. (a) Like the standard genetic code, the class of simple amino acids has more assignments than the complex and sulfur classes (red). This may partly result from the fact that the simple class is more frequent among the 20 encoded amino acids, but this tendency remains even if we correct for the unequal distribution of classes (blue). (b) Like the standard genetic code, there is a positive correlation between an amino acid’s frequency in proteins, modelled in terms of probability of amino acid transfer, and number of assignments (black). And there is also a negative correlation between its molecular weight and number of assignments (purple). Again, this may partly result from the fact that lighter amino acids are more frequent among the 20 encoded amino acids.