Oligopeptides' frequencies in the classification of proteins' primary structures

This paper reports about an approach to the classification of proteins’ primary structures taking advantage of the Self Organizing Maps algorithm and of a numerical coding of the aminoacids based upon their physicochemical properties. Hydrophobicity, volume, surface area, hydrophilicity, bulkiness, refractivity and polarity were subjected to a Principal Component Analysis and the first two principal components, explaining 84.8 % of the total observed variability, were used to cluster the aminoacids into 4 or 5 classes through a k-means algorithm. This leads to an economical representation of the primary structures which, in the construction of the input vectors for the Self Organizing Maps algorithm, allows the consideration of up to triand tetrapeptides’ frequency matrices with minimal computational overload. In comparison with previously explored conditions, namely symbolic coding of aminoacids and dipeptides frequencies, no significant improvement was observed in the classification of 69 cytochromes of the c type, characterized by a high degree of structural and functional similarity, while a substantial improvement occurred in the case of a data set including quite heterogeneous primary structures.