Preliminary mutational analysis of the human kinin B<sub>2</sub>receptor for nonpeptide antagonist ligands recognition
Bibliographic record
Abstract
FR173657, LF16,0335, and LF16,0687 are nonpeptide antagonists, endowed with high affinity and selectivity for the human kinin B2 receptor. The kinin B2 receptor belongs to the family of G-protein-coupled receptors with seven transmembrane (TM) helices. In the present study, we aimed, through computer-assisted modeling and mutagenesis, to identify residues in the human B2 receptor (hB2R) amino acid sequence that are involved in nonpeptide antagonist binding in order to build up experimental data as a first step towards a molecular model of nonpeptide ligands binding site. Fourteen amino acid residues within the TM segments were mutated to alanine. The wild type and mutant receptors were stably expressed in Chinese hamster ovary (dhfr-) cells and tested for their ability to bind agonist ([3H]bradykinin) and peptide antagonist ([3H]MENI 1270) radioligands. The affinity of nonpeptide ligands was determined by heterologous competition experiments using the above radioligands. We found that some mutations in TM2 (W86A) and TM7 (Y295A, N297A) impair the binding affinity of the three nonpeptide antagonists. On the other hand, some mutated residues in TM3 (S1 17A) and TM6 (W256A) reduce the affinity of LF16,0335 and LF16,0687 only. Results are discussed in order to build up a hypothesis for the likely different interactions of various nonpeptide ligands with the B2 receptor.
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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".