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Motivation: The study of diverse enzyme superfamilies can provide importantinsight into the relationships between protein sequence, structure andfunction. It is often challenging, however, to discover these relationshipsacross a large and diverse superfamily. Contemporary similarity networkvisualization techniques allow researchers to aggregate sequence similarityinformation into a single global view. Network visualization provides aqualitative estimate of functional diversity within a superfamily, but isunable to quantitate explicit boundaries, when present, between neighboringfamilies in sequence space. This limits the potential of existingsequence-based algorithms to generate functional predictions from superfamilydatasets.  Results: By building on current network analysis tools, we have developed anew algorithm for elucidating pairs of homologous families within a sequencedataset. Our algorithm is able to filter through a dense similarity network inorder to estimate both the boundaries of individual families and also how thefamilies neighbor one another. Globally, these neighboring families define atopology across the entire superfamily. The topology is simple to interpret byvisualizing the network output generated by our filtration protocol. We havecompared the network topology within the kinase superfamily against availablephylogenetic data. Our results suggest that neighbors within the filteredkinase network are more likely to share structural and functional propertiesthan more distant network clusters.

A Network Filtration Protocol for Elucidating Relationships between Families in a Protein Similarity Network