  The similarity between protein sequences is a directly and easly computed
quantity from which to deduce information about their evolutionary distance and
to detect homologous proteins. The SIMAP database -- Similarity Matrix of
Proteins -- provides a pre-computed similarity matrix covering the similarity
space formed by about all publicly available amino acid sequences from public
databases and completely sequenced genomes. From SIMAP we construct the protein
homology network, where the proteins are the nodes and the links represent
homology relationships. With more than 5 million nodes and about 70 10^9 edges
it is the greatest protein homology network ever been builded. We describe the
basic features and we perform a global statistical analysis of the network.
Starting from the Smith-Waterman similarity score, we define for each edge a
weight w to measure the similarity distance between two nodes. Keeping only
edges with a weigth greater than a minimal w_min, and by varying w_min we build
a family of networks with different degree of similarity. We investigate the
distribution of connected components (clusters) of the networks at different
w_min and in particular we find a behaviour similar to a phase transition
guided by the formation of a giant component. Moreover we study selected
sequence features and protein domains of protein pairs that connect different
clusters in the networks at different level of similarity. We observed
specific, non-random distributions of the protein features and domains for
proteins connecting clusters at certain weight intervals.
