Journal Papers Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMS


Abstract Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence^function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based pro¢le hidden Markov model (T-HMM) is superior in generating a pro¢le for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The pro¢le generated by T-HMM gives high accuracy in GPCR function classi¢cation, both by ligand and by coupled G protein.

Paper Details


B. Qian,  O. Soyer,  R. Goldstein


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