Biotechnological Fluorescent Ligands of the Bradykinin B1 Receptor: Protein Ligands for a Peptide Receptor
Bibliographic record
Abstract
The bradykinin (BK) B1 receptor (B1R) is a peculiar G protein coupled receptor that is strongly regulated to the point of being inducible in immunopathology. Limited clinical evidence suggests that its expression in peripheral blood mononuclear cells is a biomarker of active inflammatory states. In an effort to develop a novel imaging/diagnostic tool, we report the rational design and testing of a fusion protein that is a ligand of the human B1R but not likely to label peptidases. This ligand is composed of a fluorescent protein (FP) (enhanced green FP [EGFP] or mCherry) prolonged at its N-terminus by a spacer peptide and a classical peptide agonist or antagonist (des-Arg9-BK, [Leu8]des-Arg9-BK, respectively). The design of the spacer-ligand joint peptide was validated by a competition assay for [3H]Lys-des-Arg9-BK binding to the human B1R applied to 4 synthetic peptides of 18 or 19 residues. The labeling of B1R-expressing cells with EGFP or mCherry fused with 7 of such peptides was performed in parallel (microscopy). Both assays indicated that the best design was FP-(Asn-Gly)n-Lys-des-Arg9-BK; n = 15 was superior to n = 5, suggesting benefits from minimizing steric hindrance between the FP and the receptor. Cell labeling concerned mostly plasma membranes and was inhibited by a B1R antagonist. EGFP-(Asn-Gly)15-Lys-des-Arg9-BK competed for the binding of [3H]Lys-des-Arg9-BK to human recombinant B1R, being only 10-fold less potent than the unlabeled form of Lys-des-Arg9-BK to do so. The fusion protein did not label HEK 293a cells expressing recombinant human BK B2 receptors or angiotensin converting enzyme. This study identifies a modular C-terminal sequence that can be adapted to protein cargoes, conferring high affinity for the BK B1R, with possible applications in diagnostic cytofluorometry, histology and drug delivery (e.g., in oncology).
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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 | 0.001 | 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".