Characterizing clinically relevant natural variants of GPCRs using computational approaches.

ABSTRACT

G protein-coupled receptors (GPCRs) are an important class of drug targets owing to their physiological role. A large number of clinically relevant single nucleotide polymorphisms (SNPs) have been observed in GPCRs that are linked to disease susceptibility and adverse drug response. It is therefore important to characterize the variants in order to improve GPCR therapeutics. Here, we discuss computational methods coupling molecular dynamics simulations with docking and free energy calculations to characterize the functional differences in GPCR variants. The hallmark of this approach is the explicit incorporation of receptor and membrane dynamics that allows us to analyze short- and long-range effects in the variant receptors. We use the SNPs reported in ß2-adrenergic receptor (ß2AR) as a test case and highlight the recent successes in analyzing structural and dynamic differences in a series of population variants. The computational approach we discuss here has a twofold benefit it helps to unravel the molecular mechanisms underlying hypo- or hyperfunctionality of variant receptors as well as prioritizing novel variants that must be experimentally tested.