Reverse-engineering β-Arrestin Bias in the δ-Opioid Receptor

G protein–coupled receptors (GPCRs) form the largest family of cell-surface receptors and remain prime targets in drug discovery. A central challenge in modern GPCR drug discovery is understanding and exploiting biased agonism: the ability of ligands to favor signaling via therapeutically beneficial pathways while avoiding those that trigger side effects. Therefore, pinpointing the structural determinants of signaling bias is crucial for rational drug design. Biased agonists are particularly compelling for targeting opioid receptors, as in this family, ligands that limit β-arrestin (β-arr) recruitment are believed to preserve analgesia while reducing respiratory depression and addiction liabilities.

Here, we use extensive all-atom molecular dynamics (MD) simulations to dissect signaling bias in the δ-opioid receptor (δOR). Focusing on a receptor mutant with a strong β-arr bias, we employed a reverse-engineering approach to reveal the conformational mechanisms that promote β-arr recruitment. Building on these insights, engineer new mutations that reshape the receptor’s signaling profile. Importantly, this approach allowed us to pinpoint signaling bias to motions of a single microswitch and identify how structural receptor motions induced by the mutations and ligand contacts cooperate to promote a specific functional response. In this proof-of-concept study, we not only provide structural insights into δOR pharmacology but also demonstrate how computational methods can be leveraged to probe structural mechanisms of signaling specificity across GPCRs, paving the way for the rational design of tailored receptor variants and novel, safer, and more effective therapeutics.