  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 physico-chemical
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 tri- and
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.
