  Motivation: The study of diverse enzyme superfamilies can provide important
insight into the relationships between protein sequence, structure and
function. It is often challenging, however, to discover these relationships
across a large and diverse superfamily. Contemporary similarity network
visualization techniques allow researchers to aggregate sequence similarity
information into a single global view. Network visualization provides a
qualitative estimate of functional diversity within a superfamily, but is
unable to quantitate explicit boundaries, when present, between neighboring
families in sequence space. This limits the potential of existing
sequence-based algorithms to generate functional predictions from superfamily
datasets.
  Results: By building on current network analysis tools, we have developed a
new algorithm for elucidating pairs of homologous families within a sequence
dataset. Our algorithm is able to filter through a dense similarity network in
order to estimate both the boundaries of individual families and also how the
families neighbor one another. Globally, these neighboring families define a
topology across the entire superfamily. The topology is simple to interpret by
visualizing the network output generated by our filtration protocol. We have
compared the network topology within the kinase superfamily against available
phylogenetic data. Our results suggest that neighbors within the filtered
kinase network are more likely to share structural and functional properties
than more distant network clusters.
