Classifying proteins using gapped Markov feature pairs

Classification of protein sequences has important applications in areas such as disease diagnosis, treatment development and drug design. In this paper we present a highly accurate classifier called the g-MARS (gapped Markov Chain with support vector machine) protein classifier. It models the structure of a protein sequence by measuring the transition probabilities between pairs of amino acids. This results in a Markov chain style model for each protein sequence. Then, to capture the similarity among non-exactly matching protein sequences, we show that this model can be generalized to incorporate gaps in the Markov chain. Theoretical justification for the power of our gapped feature space model is provided through its connections to analysis methods for nonlinear dynamical systems. We perform an experimental study and compare g-MARS to several other state-of-the-art protein classifiers. Overall, we demonstrate that g-MARS has high accuracy and operates efficiently on a diverse range of protein families.