title: Top-down clustering for protein subfamily identification.
abstract: We propose a novel method for the task of protein subfamily identification; that is, finding subgroups of functionally closely related sequences within a protein family. In line with phylogenomic analysis, the method first builds a hierarchical tree using as input a multiple alignment of the protein sequences, then uses a post-pruning procedure to extract clusters from the tree. Differently from existing methods, it constructs the hierarchical tree top-down, rather than bottom-up and associates particular mutations with each division into subclusters. The motivating hypothesis for this method is that it may yield a better tree topology with more accurate subfamily identification as a result and additionally indicates functionally important sites and allows for easy classification of new proteins. A thorough experimental evaluation confirms the hypothesis. The novel method yields more accurate clusters and a better tree topology than the state-of-the-art method SCI-PHY, identifies known functional sites, and identifies mutations that alone allow for classifying new sequences with an accuracy approaching that of hidden Markov models.
authors.affiliation: Department of Computer Science, KU Leuven, Belgium.
authors.forename: Eduardo P
authors.initials: EP
authors.lastname: Costa
authors.forename: Celine
authors.initials: C
authors.lastname: Vens
authors.forename: Hendrik
authors.initials: H
authors.lastname: Blockeel
keywords: clustering trees; decision trees; phylogenomics; protein subfamily identification; top-down clustering