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The goal of protein family classification is to group proteins into families so that proteins within the same family have common function or are related by ancestry. While supervised classification algorithms are available for this purpose, most of these approaches focus on assigning unclassified proteins to known families but do not allow for progressive construction of new families from proteins that cannot be assigned. Although unsupervised clustering algorithms are also available, they do not make use of information from known families. By computing similarities between proteins based on pairwise sequence comparisons, we develop supervised classification algorithms that achieve improved accuracy over previous approaches while allowing for construction of new families. We show that our algorithm has higher accuracy rate and lower mis-classification rate when compared to algorithms that are based on the use of multiple sequence alignments and hidden Markov models, and our algorithm performs well even on families with very few proteins and on families with low sequence similarity. A software program implementing the algorithm (SClassify) is available online (http://faculty.cse.tamu.edu/shsze/sclassify).

Supervised Protein Family Classification and New Family Construction