Using silico methods predicting ligands for orphan GPCRs.
The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.
Jiang
Zhenran
Z
Hubei Bioinformatics and Molecular Imaging Key Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Zhou
Yanhong
Y
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D006801:Humans; D008024:Ligands; D011485:Protein Binding; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism*
D008024:Ligands; D043562:Receptors, G-Protein-Coupled