Atomistic Modeling of Neuro-cardiovascular Coupling Modulation

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Cardiac heart rhythm and vascular tone are tightly controlled by sympathetic and parasympathetic autonomic nervous systems and their dysregulation is associated with multiple cardiovascular disorders such as arrythmias and hypertension. At the molecular level such control is established through a complex set of events including synaptic neurotransmitter release, binding to myocyte adrenergic or cholinergic G-protein coupled receptors (GPCRs), subcellular signal transduction through G-proteins, neurotransmitter - GPCR unbinding, reuptake or degradation. In this study, funded by NIH Common Fund program “Stimulating Peripheral Activity to Relieve Conditions” (SPARC), we used atomistic modeling to identify structural, energetic and kinetic determinants of several molecular processes crucial for cardiovascular neuromodulation such as neurotransmitter interactions with GPCR / G-protein complex at neuroeffector junction as well as G-protein interaction with adenylyl cyclase (AC), a key enzyme for downstream subcellular signaling. To this end, we developed atomistic models of human β-adrenergic receptor (βAR) / Gs and muscarinic receptor (MR) / Gi GPCR / G-protein complexes in different conformational states using available structures and Rosetta structural modeling. We tested these models via molecular docking of βAR agonists norepinephrine and isoproterenol and MR agonists acetylcholine and carbachol. Moreover, receptor model structural stabilities were assessed via microsecond-long all-atom molecular dynamics (MD) simulations with and without ligand bound. Enhanced sampling atomistic MD runs were used to estimate receptor - ligand binding affinities and association / dissociation rates. Martini coarse-grained and Brownian dynamics implicit-solvent techniques were utilized to assess G-protein interactions with AC and GPCR as well. This information will be used to inform functional kinetic models of autonomic control of myocyte subcellular signaling, a crucial component of our predictive multi-scale neurocardiovascular simulator.