Synthesis of an Agonistic, Difluoro-Azido Photolabel of Angiotensin II and Labeling of the AT<sub>1</sub>Receptor: Transmembrane Domains 3, 6, and 7 Form the Ligand-Binding Pocket
Bibliographic record
Abstract
p-Azido-phenylalanine has been frequently used for photoaffinity labeling of target proteins such as the angiotensin receptors. However, chemical studies showed that simple aryl nitrenes first react intramolecularly, forming a semistable cyclic keteneimine and then reacting with nucleophile residues in the target structure like those of lysine and arginine. We synthesized 3,5-difluoro-4-azidophenylalanine where the formation of the keteneimine is prevented and where photoincorporation should be due to nonselective nitrene insertion only. This new amino acid was introduced in position 8 of angiotensin II and compared with the corresponding azidophenylalanine peptide using human AT1 receptor as target. The new photolabel maintained full agonist activity and a similar yield of photolabeling but without the previously observed gradual hydrolysis. Several selective proteolyses of the labeled receptor indicate that the new photolabel forms three simultaneous contact regions on the hAT1 receptor, suggestive of a nonselective behavior of the photolabel. A major contact was established in the sixth transmembrane domain but also in the third and seventh domain. Our results are in excellent agreement with those recently obtained from methionine proximity assay studies.
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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 | 0.000 |
| 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 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".