Design and Synthesis of Photoaffinity-Labeling Ligands of the<scp>l</scp>-Prolyl-<scp>l</scp>-leucylglycinamide Binding Site Involved in the Allosteric Modulation of the Dopamine Receptor
Bibliographic record
Abstract
Pro-Leu-Gly-NH(2) (PLG), in addition to its endocrine effects, possesses the ability to modulate dopamine D(2) receptors within the central nervous system. However, the precise binding site of PLG is unknown. Potential photoaffinity-labeling ligands of the PLG binding site were designed as tools to be used in the identification of the macromolecule that possesses this binding site. Six different photoaffinity-labeling ligands were designed and synthesized on the basis of the gamma-lactam PLG peptidomimetic 1. The 4-azidobenzoyl and 4-azido-2-hydroxybenzoyl photoaffinity-labeling moieties were placed at opposite ends of PLG peptidomimetic 1 to generate a series of ligands that potentially could be used to map the PLG binding site. All of the compounds that were synthesized possessed activity comparable to or better than PLG in enhancing [(3)H]-N-propylnorapomorphine agonist binding to dopamine receptors. Photoaffinity ligands that were cross-linked to the receptor preparation produced a modulatory effect that was either comparable to or greater than the increase in agonist binding produced by the respective ligands that were not cross-linked to the dopamine receptor. The results indicate that these photoaffinity-labeling agents are binding at the same allosteric site as PLG and PLG peptidomimetic 1.
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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 | 0.001 |
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".