Unveiling G-Protein-Coupled Receptor Conformational Dynamics via Metadynamics Simulations and Markov State Models
The dynamic character of G-protein-coupled receptors (GPCRs) is essential in their functionality as signal transducers. However, the molecular details of how ligands affect this conformational repertoire to steer intracellular signaling pathways remain elusive. Here, we address this question by modeling the conformational landscape of the growth hormone secretagogue receptor (GHSR-1a), a prototypical peptide-activated class A GPCR, in its apo state and bound to pharmacologically distinct ligands. We present a generally applicable protocol to efficiently explore the conformational space of GHSR-1a that is sensitive to the bound ligand. Combining metadynamics simulations and Markov state modeling, we computed the free energy landscape of GHSR-1a in its apo state and bound to an agonist, antagonist, or inverse agonist, respectively. Consistent with the current multi-state model of GPCR activity, we found that GHSR-1a populates multiple metastable states whose energies and transition probabilities change depending on the bound ligand. Furthermore, our Markov state models (MSM) have revealed two intermediate states that have not yet been described by experimental structures and which we assume to facilitate the binding of extracellular ligands and intracellular protein partners, respectively. Lastly, our MSMs allowed us to shed light on the molecular differences between basal and agonistinduced GHSR-1a activation. Our results are not only compatible with previously reported experimental data, but they capture the equilibria governing GHSR-1a activation in unprecedented detail. Due to its applicability to all class A GPCRs, our protocol is a valuable tool for the development of pharmaceuticals targeting this protein family.