Adenosine A2A-Dopamine D2 Receptor-Receptor Heteromerization
Bibliographic record
Abstract
There is evidence for strong functional antagonistic interactions between adenosine A2A receptors (A2ARs) and dopamine D2 receptors (D2Rs). Although a close physical interaction between both receptors has recently been shown using co-immunoprecipitation and co-localization assays, the existence of a A2AR-D2R protein-protein interaction still had to be demonstrated in intact living cells. In the present work, fluorescence resonance energy transfer (FRET) and bioluminescence resonance energy transfer (BRET) techniques were used to confirm the occurrence of A2AR-D2R interactions in co-transfected cells. The degree of A2AR-D2R heteromerization, measured by BRET, did not vary after receptor activation with selective agonists, alone or in combination. BRET competition experiments were performed using a chimeric D2R-D1R in which helices 5 and 6, the third intracellular loop (I3), and the third extracellular loop (E3) of the D2R were replaced by those of the dopamine D1 receptor (D1R). Although the wild type D2R was able to decrease the BRET signal, the chimera failed to achieve any effect. This suggests that the helix 5-I3-helix 6-E3 portion of D2R holds the site(s) for interaction with A2AR. Modeling of A2AR and D2R using a modified rhodopsin template followed by molecular dynamics and docking simulations gave essentially two different possible modes of interaction between D2R and A2AR. In the most probable one, helix 5 and/or helix 6 and the N-terminal portion of I3 from D2R approached helix 4 and the C-terminal portion of the C-tail from the A2AR, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".