Classification of GPCRs Using Family Specific Motifs

Cobanoglu M. C., Saygin Y., Sezerman U.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, cilt.8, sa.6, ss.1495-1508, 2011 (SCI-Expanded) identifier identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8 Sayı: 6
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1109/tcbb.2010.101
  • Dergi Adı: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1495-1508
  • Anahtar Kelimeler: Sequence analysis, GPCR classification, data mining, motif selection, PROTEIN-COUPLED RECEPTORS, HIDDEN MARKOV MODEL, ALLOSTERIC MODULATORS, PREDICTION, SYSTEM
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Hayır

Özet

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.