Discovery of A class GPCR ligands for probing signal transduction pathways

Frontiers in Pharmacology

Canan G. Nebigil-Desaubry¹  Laurent G. Désaubry²  Andrea eTafi³  Simone eBrogi³ 

[1] Biotechnology and Cell Signaling Laboratory, UMR 7242, CNRS/University of Strasbourg, LabEx Medalis;Therapeutic Innovation Laboratory, UMR7200, CNRS/University of Strasbourg;University of Siena;

关键词: Pharmacology;    GPCR;    Biased Competition;    ligand-receptor interaction;    Drug Discovery Screening;   

DOI  :  10.3389/fphar.2014.00255

来源: DOAJ

【 摘 要 】

G protein–coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics (MD) simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design (SBDD) and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as 3D-QSAR, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCRs ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

【 授权许可】

Unknown