Title :
Classification of GPCRs Using Family Specific Motifs
Author :
Cobanoglu, Murat Can ; Saygin, Yucel ; Sezerman, Ugur
Author_Institution :
MDBF, Sabanci Univ., Istanbul, Turkey
Abstract :
The classification of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number of drug-targeted receptors. In this work, we propose a method for Class A subfamily classification using sequence-derived motifs which characterizes the subfamilies by discovering receptor-ligand interaction sites. The motifs that best characterize a subfamily are selected by the Distinguishing Power Evaluation (DPE) technique we propose. The experiments performed on GPCR sequence databases show that our method outperforms state-of-the-art classification techniques for GPCR Class A subfamily prediction. An important contribution of our work is to discover key receptor-ligand interaction sites which is very important for drug design.
Keywords :
biological techniques; biology computing; molecular biophysics; molecular configurations; proteins; GPCR class A; GPCR class A subfamily prediction; GPCR classification; GPCR sequence databases; class A subfamily classification; distinguishing power evaluation technique; drug design; family specific motifs; ligand interaction; protein coupled receptor sequences; receptor-ligand interaction sites; sequence-derived motifs; state-of-the-art classification techniques; Amino acids; Classification; Drugs; Hidden Markov models; Proteins; Sequential analysis; Support vector machines; GPCR classification; Sequence analysis; data mining; motif selection.; Amino Acid Motifs; Binding Sites; Computational Biology; Drug Design; Ligands; Models, Molecular; Receptors, G-Protein-Coupled; Support Vector Machines;
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
DOI :
10.1109/TCBB.2010.101