A peptide targeting an interaction interface disrupts the dopamine D1‐D2 receptor heteromer to block signaling and function <i>in vitro</i> and <i>in vivo:</i> effective selective antagonism
Bibliographic record
Abstract
Although the dopamine D1-D2 receptor heteromer has emerging physiological relevance and a postulated role in different neuropsychiatric disorders, such as drug addiction, depression, and schizophrenia, there is a need for pharmacological tools that selectively target such receptor complexes in order to analyze their biological and pathophysiological functions. Since no selective antagonists for the D1-D2 heteromer are available, serial deletions and point mutations were used to precisely identify the amino acids involved in an interaction interface between the receptors, residing within the carboxyl tail of the D1 receptor that interacted with the D2 receptor to form the D1-D2 receptor heteromer. It was determined that D1 receptor carboxyl tail residues (404)Glu and (405)Glu were critical in mediating the interaction with the D2 receptor. Isolated mutation of these residues in the D1 receptor resulted in the loss of agonist activation of the calcium signaling pathway mediated through the D1-D2 receptor heteromer. The physical interaction between the D1 and D2 receptor could be disrupted, as shown by coimmunoprecipitation and BRET analysis, by a small peptide generated from the D1 receptor sequence that contained these amino acids, leading to a switch in G-protein affinities and loss of calcium signaling, resulting in the inhibition of D1-D2 heteromer function. The use of the D1-D2 heteromer-disrupting peptide in vivo revealed a pathophysiological role for the D1-D2 heteromer in the modulation of behavioral despair. This peptide may represent a novel pharmacological tool with potential therapeutic benefits in depression treatment.
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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) | 0.001 | 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".