METHIONINE PROXIMITY ASSAY, A NOVEL METHOD FOR EXPLORING PEPTIDE LIGAND–RECEPTOR INTERACTION
Bibliographic record
Abstract
Probing G-protein coupled receptor (GPCR) structures is a priority in the functional and structural understanding of GPCRs. In the past, we have used several approaches around photoaffinity labeling in order to establish contact points between peptide ligands and their cognate receptors. Such contact points are helpful to build reality based molecular models of GPCRs and to elucidate their activation mechanisms. Most studies of peptidergic GPCRs have been done with photolabeling peptides containing the benzophenone moiety as a reputedly non-selective probe. However our recent results are now showing that p-benzoylphenylalanine (Bpa) has some selectivity for Met residues in the receptor protein, reducing the accuracy of this method. Turning a problem into an asset, modified analogues of Bpa, e.g. p,p'-nitrobenzoylphenylalanine (NO2Bpa), display increased selectivity for such Met residues. It means a photoprobe containing such modified benzophenone-moieties does not label a receptor protein unless a Met residue is in the immediate vicinity. This unique property allows us to propose and show the feasibility and utility of a new method for scanning the contact areas of peptidergic GPCRs, the Methionine Proximity Assay (MPA). Putative contact residues of the receptor are exchanged to Met residues by site-directed mutagenesis and are subjected to photoaffinity labeling with such modified benzophenone-containing peptides. Successful incorporation indicates physical proximity of those residues. This principle is established and explored with benzophenone-containing analogues of angiotensin II and the two known human angiotensin II receptors AT1 and AT2, determining contact points in both receptors. This approach has several important advantages over other scanning approaches, e.g., the SCAM procedure, since the MPA-method can be used in the hydrophobic core of receptors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".