Reverse-engineering β-Arrestin Bias in the δ-Opioid Receptor
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".