Deciphering Opioid Peptide Binding Modes at Atypical Chemokine Receptor 3
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide ACKR3 is a class A G protein-coupled receptor that is considered as an atypical chemokine receptor. It does not activate G proteins but efficiently recruits β-arrestin and mediates ligand internalization and was thus proposed as a scavenger receptor. Besides chemokines, ACKR3 internalizes a variety of endogenous opioid peptides, including adrenorphin and dynorphin A. By reducing their availability to the classical opioid receptors, ACKR3 is proposed to participate in the endogenous pain management system, suggesting it as a new potential target for a new class of analgesics. Available structural data for ACKR3 are focused on the binding of chemokines (e.g., CXCL12), but how opioid peptides bind at ACKR3 remains enigmatic. Here, we structurally modeled opioid peptide binding at ACKR3 with a focus on adrenorphin, its ACKR3 selective variant LIH383, and dynorphin A. By combining molecular dynamics simulations with pharmacophore analysis, we analyze the opioid peptides’ binding modes and compare them with binding to classical opioid receptors (MOR, KOR, and DOR). We apply our model to rationally explain previously reported structure–activity relationships for adrenorphin derivatives, which also supports the model’s validation. Moreover, we include in vitro ACKR3 mutational experiments on both the receptor and LIH383 to further strengthen our structural model. Taken together, we systematically combine in silico observations and in vitro readouts to contribute to the understanding of ACKR3's ligand binding profile and set the basis for further ACKR3 ligand development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".