Determining amino acid scores of the genetic code table: Complementarity, structure, function and evolution

Abstract

The Standard Genetic Code (SGC) table was investigated with respect to the three-dimensional codon arrangement, and all possible 24 hierarchical base partitions (4! = 24). This was done by determining the amino acid scores for each codon hierarchy in relation to the 1st horizontal, 2nd vertical and 3rd horizontal sub-tables.Marked differences were observed for the hydrophobicity and lipophilicity parameters encoded by the second base of the SGC table. The nucleotide hierarchy U < C < G < A and its complement A < G < C < U at the second base correlated best with the amino acid hydrophobicity and polarity. By contrast, the hierarchy C < G < U < A and its backwards transcript A < U < G < C at the second base were associated with the amino acid parameters of lipophilicity and accessible surface area.No association was observed between 24 base hierarchies of the codons at the 1st and 3rd positions with respect to the hydropathy, polarity, lipophilicity and accessible surface area. The results imply that the second base possesses the majority of information content with respect to the physicochemical properties observed.It is shown that amino acid information obtained by determining the scores of the bases and codon weightings in digital form coincides with physicochemical properties, and the temperature range between 25 °C and 100 °C does not affect the hydrophobicity, the related prediction of α- and β-protein structure, codon scores, or the complementarity code for sense and antisense peptide interactions.The amino acid scores determined for the SGC table enable the construction of rules and algorithms for the analysis of the structure, function and evolution of proteins. It has been demonstrated that IUPAC-based encoding of nucleobase and amino acid sequences could be used for the representation of the bases with the Semiotic (Greimas) Square and probabilistic square of opposition.It is concluded that the structural, functional and evolutionary patterns of the protein sequences may be modeled using codon based amino acid information, instead of using the information based on amino acid physicochemical properties only.