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On the optimality of the genetic code, with the consideration of coevolution theory by comparison of prominent cost measure matrices

Goodarzi, H ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jtbi.2005.01.012
  3. Publisher: 2005
  4. Abstract:
  5. Statistical and biochemical studies have revealed non-random patterns in codon assignments. The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslation errors and point mutations, since it is known that when an amino acid is converted to another due to error, the biochemical properties of the resulted amino acid are usually very similar to those of the original one. In this study, using altered forms of the fitness functions used in the prior studies, we have optimized the parameters involved in the calculation of the error minimizing property of the genetic code so that the genetic code outscores the random codes as much as possible. This work also compares two prominent matrices, the Mutation Matrix and Point Accepted Mutations 74-100 (PAM74-100). It has been resulted that the hypothetical properties of the coevolution theory of the genetic code are already considered in PAM74-100, giving more evidence on the existence of bias towards the genetic code in this matrix. Furthermore, our results indicate that PAM74-100 is biased towards the single base mistranslation occurrences in second codon position as well as the frequency of amino acids. Thus PAM74-100 is not a suitable substitution matrix for the studies conducted on the evolution of the genetic code. © 2005 Elsevier Ltd. All rights reserved
  6. Keywords:
  7. Coevolution ; Fitness ; Amino acid analysis ; Amino acid substitution ; Analytical error ; Canonical analysis ; Codon usage ; Cost ; Genetic code ; Molecular evolution ; Physical chemistry ; Point mutation ; Priority journal ; Randomization ; RNA translation ; Sequence homology ; Statistical analysis ; Theory ; Animals ; Codon ; Evolution, Molecular ; Gene frequency ; Models, Genetic
  8. Source: Journal of Theoretical Biology ; Volume 235, Issue 3 , 2005 , Pages 318-325 ; 00225193 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0022519305000378?via%3Dihub