Title: Understanding Functional Residues of the Cannabinoid CB1 Receptor for Drug Discovery
Volume: 10
Issue: 8
Author(s): Joong-Youn Shim
Affiliation:
Keywords:
G protein-coupled receptor (GPCR), the brain cannabinoid (CB1) receptor, functional residues, mechanism of receptor activation, structure-based drug design
Abstract: The brain cannabinoid (CB1) receptor that mediates numerous physiological processes in response to marijuana and other psychoactive compounds is a G protein-coupled receptor (GPCR) and shares common structural features with many rhodopsin class GPCRs. For the rational development of therapeutic agents targeting the CB1 receptor, understanding the ligand-specific CB1 receptor interactions responsible for unique G protein signals is crucial. For more than a decade, a combination of mutagenesis and computational modeling approaches has been successfully employed to study the ligand-specific CB1 receptor interactions. In this review, after a brief discussion about recent advances in understanding of some structural and functional features of GPCRs commonly applicable to the CB1 receptor, the CB1 receptor functional residues reported from mutational studies are divided into three different types, ligand binding (B), receptor stabilization (S) and receptor activation (A) residues, to delineate the nature of the binding pockets of anandamide, CP55940, WIN55212-2 and SR141716A and to describe the molecular events of the ligand-specific CB1 receptor activation from ligand binding to G protein signaling. Taken these CB1 receptor functional residues, some of which are unique to the CB1 receptor, together with the biophysical knowledge accumulated for the GPCR active state, it is possible to propose the early stages of the CB1 receptor activation process that not only provide some insights into understanding molecular mechanisms of receptor activation but also are applicable for identifying new therapeutic agents by applying the validated structure-based approaches, such as virtual high throughput screening (HTS) and fragment-based approach (FBA).
