Photolabelling the urotensin II receptor reveals distinct agonist- and partial-agonist-binding sites
Bibliographic record
Abstract
The mechanism by which GPCRs (G-protein-coupled receptors) undergo activation is believed to involve conformational changes following agonist binding. We have used photoaffinity labelling to identify domains within GPCRs that make contact with various photoreactive ligands in order to better understand the activation mechanism. Here, a series of four agonist {[Bpa1]U-II (Bpa is p-benzoyl-L-phenylalanine), [Bpa2]U-II, [Bpa3]U-II and [Bpa4]U-II} and three partial agonist {[Bpa1Pen5D-Trp7Orn8]U-II (Pen is penicillamine), [Bpa2Pen5D-Trp7Orn8]U-II and [Pen5Bpa6D-Trp7Orn8]U-II} photoreactive urotensin II (U-II) analogues were used to identify ligand-binding sites on the UT receptor (U-II receptor). All peptides bound the UT receptor expressed in COS-7 cells with high affinity (Kd of 0.3-17.7 nM). Proteolytic mapping and mutational analysis led to the identification of Met288 of the third extracellular loop of the UT receptor as a binding site for all four agonist peptides. Both partial agonists containing the photoreactive group in positions 1 and 2 also cross-linked to Met288. We found that photolabelling with the partial agonist containing the photoreactive group in position 6 led to the detection of transmembrane domain 5 as a binding site for that ligand. Interestingly, this differs from Met184/Met185 of the fourth transmembrane domain that had been identified previously as a contact site for the full agonist [Bpa6]U-II. These results enable us to better map the binding pocket of the UT receptor. Moreover, the data also suggest that, although structurally related agonists or partial agonists may dock in the same general binding pocket, conformational changes induced by various states of activation may result in slight differences in spatial proximity within the cyclic portion of U-II analogues.
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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".