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Motivation: A large fraction of biological research concentrates on individual proteins and on small families of proteins. One of the current major challenges in bioinformatics is to extend our knowledge to very large sets of proteins. Several major projects have tackled this problem. Such undertakings usually start with a process that clusters all known proteins or large subsets of this space. Some work in this area is carried out automatically, while other attempts incorporate expert advice and annotation.
Results: We propose a novel technique that automatically clusters protein sequences. We consider all proteins in SWISSPROT, and carry out an all-against-all BLAST similarity test among them. With this similarity measure in hand we proceed to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a hierarchy of clusters of varying degrees of granularity. Here we compare the clusters that result from alternative merging rules, and validate the results against InterPro.
Our preliminary results show that clusters that are consistent with several rather than a single merging rule tend to comply with InterPro annotation. This is an affirmation of the view that the protein space consists of families that differ markedly in their evolutionary conservation.
Availability: The outcome of these investigations can be viewed in an interactive Web site at http://www.protonet.cs.huji.ac.il
Supplementary information: Biological examples for comparing the performance of the different algorithms used for classification are presented in http://www.protonet.cs.huji.ac.il/examples.html
Contact: ori@cs.huji.ac.il
Keywords: protein families; protein classification; sequence alignment; clustering.

The ProtoMap site offers an exhaustive classification of all proteins in the SWISS-PROT database, into groups of related proteins. The classification is based on analysis of all pairwise similarities among protein sequences. The analysis makes essential use of transitivity to identify homologies among proteins. Within each group of the classification, every two members are either directly or transitively related. However, transitivity is applied restrictively in order to prevent unrelated proteins from clustering together. The classification is done at different levels of confidence, and yields a hierarchical organization of all proteins. The resulting classification splits the protein space into well-defined groups of proteins, which are closely correlated with natural biological families and superfamilies. Many clusters contain protein sequences that are not classified by other databases. The hierarchical organization suggested by our analysis may help in detecting finer subfamilies in families of known proteins. In addition it brings forth interesting relationships between protein families, upon which local maps for the neighborhood of protein families can be sketched. The ProtoMap web server can be accessed at http://www.protomap.cs.huji.ac.il

LetAbe a subset of the unit ball in ℝn, and let 0≤r≤1 be real. Find a pointxfor which the intersection of ther-neighborhood ofxwithAhas a large measure. Tight bounds on this measure are found.