  In this paper, we address the problem of identifying protein functionality
using the information contained in its aminoacid sequence. We propose a method
to define sequence similarity relationships that can be used as input for
classification and clustering via well known metric based statistical methods.
In our examples, we specifically address two problems of supervised and
unsupervised learning in structural genomics via simple metric based techniques
on the space of trees 1)Unsupervised detection of functionality families via K
means clustering in the space of trees, 2)Classification of new proteins into
known families via k nearest neighbour trees. We found evidence that the
similarity measure induced by our approach concentrates information for
discrimination. Classification has the same high performance than others VLMC
approaches. Clustering is a harder task, though, but our approach for
clustering is alignment free and automatic, and may lead to many interesting
variations by choosing other clustering or classification procedures that are
based on pre-computed similarity information, as the ones that performs
clustering using flow simulation, see (Yona et al 2000, Enright et al, 2003).
