Automated Protein Subfamily Identification and Classification - Brown, Duncan P; Krishnamurthy, Nandini; Sjölander, Kimmen (PLOS Computational Biology, 2007)
Author SummaryPredicting the function of a gene or protein (gene product) from its primary sequence is a major focus of many bioinformatics methods. In this paper, the authors present a three-stage computational pipeline for gene functional annotation in an evolutionary framework to reduce the systematic errors associated with the standard protocol (annotation transfer from predicted homologs). In the first stage, a functional hierarchy is estimated for each protein family and subfamilies are identified. In the second stage, hidden Markov models (HMMs) (a type of statistical model) are constructed for each subfamily to model both the family-defining and subfamily-specific signatures. In the third stage, subfamily HMMs are used to assign novel sequences to functional subtypes. Extensive experimental validation of these methods shows that predicted subfamilies correspond closely to functional subtypes identified by experts and to conserved clades in phylogenetic trees; that subfamily HMMs increase the separation between homologs and non-homologs in sequence database discrimination tests relative to the use of a single HMM for the family; and that specificity of classification of novel sequences to subfamilies using subfamily HMMs is near perfect (1.5% error rate when sequences are assigned to the top-scoring subfamily, and <0.5% error rate when logistic regression of scores is employed).