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Depicting a protein's two faces: GPCR classification by phylogenetic tree‐based HMMs
Author(s) -
Qian Bin,
Soyer Orkun S.,
Neubig Richard R.,
Goldstein Richard A.
Publication year - 2003
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(03)01112-8
Subject(s) - phylogenetic tree , hidden markov model , tree (set theory) , g protein coupled receptor , computational biology , sequence (biology) , multiple sequence alignment , function (biology) , artificial intelligence , biology , pattern recognition (psychology) , sequence analysis , sequence alignment , evolutionary biology , bioinformatics , computer science , genetics , mathematics , peptide sequence , gene , receptor , combinatorics
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 profile hidden Markov model (T‐HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein‐coupled receptors (GPCRs). The profile generated by T‐HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.