Distinct binding conformations of epinephrine with α- and β-adrenergic receptors

Abstract

Agonists targeting α2-adrenergic receptors (ARs) are used to treat diverse conditions, including hypertension, attention-deficit/hyperactivity disorder, pain, panic disorders, opioid and alcohol withdrawal symptoms, and cigarette cravings. These receptors transduce signals through heterotrimeric Gi proteins. Here, we elucidated cryo-EM structures that depict α2A-AR in complex with Gi proteins, along with the endogenous agonist epinephrine or the synthetic agonist dexmedetomidine. Molecular dynamics simulations and functional studies reinforce the results of the structural revelations. Our investigation revealed that epinephrine exhibits different conformations when engaging with α-ARs and β-ARs. Furthermore, α2A-AR and β1-AR (primarily coupled to Gs, with secondary associations to Gi) were compared and found to exhibit different interactions with Gi proteins. Notably, the stability of the epinephrine–α2A-AR–Gi complex is greater than that of the dexmedetomidine–α2A-AR–Gi complex. These findings substantiate and improve our knowledge on the intricate signaling mechanisms orchestrated by ARs and concurrently shed light on the regulation of α-ARs and β-ARs by epinephrine. Our bodies have a system, the sympathetic nervous system, that uses certain chemicals to control heart rate, blood pressure, etc. These chemicals, epinephrine and norepinephrine, work by activating proteins known as adrenergic receptors. Understanding these receptors could help treat diseases like high blood pressure and ADHD. This study used a method called cryo-electron microscopy to see how epinephrine interacts with these receptors. It compared how epinephrine and a similar drug, dexmedetomidine, interact with the receptors. The study found that epinephrine binds to the α and β types of the receptors differently, which could explain their different effects. This helps us understand how drugs that mimic or block epinephrine can treat diseases. This could lead to new, more effective drugs. Future research may use these findings to design better treatments for heart diseases and other conditions. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.