G-protein coupled receptors (GPCRs) are membrane proteins that bind a variety of ligands and modulate many important biological signal cascades. Understanding ligand-biased functional dynamics of these GPCRs can direct the design of more effective and targeted drugs. Recent GPCR structures give opportunity for computational study of receptor dynamics. This research project studies two GPCRs, the 2 adrenergic receptor (2AR) and the A2A adenosine receptor (A2AR) with simulations of the proteins embedded in detergent micelles, a membrane mimetic that is often used in biophysical experiments. The specific aims are: 1) To predict the full activation and deactivation mechanism for 2AR and A2AR. With unique distributed computing architectures, we will run massively parallel all-atom, explicit solvent, explicit lipid molecular dynamics simulations of 2AR and A2AR bound to a variety of ligands. With robustly parameterized simulations at unprecedented timescales, we expect to predict the inactive-to-active transitions for these GPCRs for the first time. We use statistical analysis with Markov State Models (MSMs) and Transition Path Theory to reveal the most probable pathways and the kinetics of 2AR and A2AR activation and deactivation. These methods allow us to quantify ligand-induced population shifts that select the G-protein and, for 2AR, the -arrestin pathways from the conformational landscape. 2) To investigate allosteric modulation of 2AR and A2AR conformational landscapes. Allosteric modulation of GPCRs is very attractive due to the ubiquity of the receptor active sites and the diverse conformational tuning, which can be utilized to give site-specific and event specific action. However, no selective small molecule allosteric modulators have been identified for 2AR and A2AR. We plan to mine the wealth of 2AR and A2AR simulation data obtained in aim (1) with MSMs and information theory methods to identify correlations and connectivity indicative of allosteric networks. These networks will guide targeted small molecule docking to putative allosteric sites. Top allosteric docking hits will be evaluated with experiments.
NIH ExPorter
NIH ExPorter