Literature DB >> 20876934

Classification of GPCRs using family specific motifs.

Murat Can Cobanoglu1, Yucel Saygin, Ugur Sezerman.   

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.

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Year:  2011        PMID: 20876934     DOI: 10.1109/TCBB.2010.101

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  2 in total

1.  GPCRsort-responding to the next generation sequencing data challenge: prediction of G protein-coupled receptor classes using only structural region lengths.

Authors:  Mehmet Emre Sahin; Tolga Can; Cagdas Devrim Son
Journal:  OMICS       Date:  2014-08-18

2.  Using random forests for assistance in the curation of G-protein coupled receptor databases.

Authors:  Aleksei Shkurin; Alfredo Vellido
Journal:  Biomed Eng Online       Date:  2017-08-18       Impact factor: 2.819

  2 in total

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