Clustering binary codes to express the biochemical properties of amino acids

We study four kinds of binary codes of amino acids (AA). Two codes of them are based respectively on biochemical properties, and the two others are generated with artificial intelligence (AI) methods, and are based on protein structures and alignment, and on Dayhoff matrix. In order to give a global significance of each binary code, we use a hierarchical clustering method to generate different clusters of each binary codes of amino acids. Each cluster is examined with biochemical properties to give an explanation on the similarity between amino acids that it contains. To validate our examination, a decision tree based machine learning system is used to characterize the AA clusters obtained with each binary codes. From this experimentation, it comes out that one of the AI based codes allows to obtain clusters that have significant biochemical properties. As a consequence, it appears that even if attributes of binary codes generated with AI methods, do not separately correspond to a biochemical property, they can be significant in the whole. Conversely binary codes based on biochemical properties can be insignificant when forming a whole.