The Importance of the N-Terminal β-Turn in Bradykinin Antagonists
Bibliographic record
Abstract
Three peptides, B-10148 (Lys-1-Lys0-Arg1-Pro2-Hyp3-Gly4-Igl5-Ser6- DF5F7-Oic8; where Hyp is trans-4-hydroxyproline, Igl is alpha-(2-indanyl)glycine, F5F is 2,3,4,5,6-pentafluorophenylalanine and Oic is (3aS,7aS)-octahydroindole-2-carboxylic acid), B-10206 (DArg0-Arg1-Pro2-Hyp3-Gly4-Igl5-Ser6-DF 5F7-Nc7G8-Arg9; where Nc7G is N-cycloheptylglycine) and B- 10284 (Arg1-Pro2-Pro3-Gly4-Phe5-Thr6-DTic7-Oic8- NH2; where Tic is 1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid), were studied in detail by NMR spectroscopy in 60% CD3OH /40% H2O and modeled by a simulated annealing protocol to determine their solution structure. B-10148, an extremely potent BK B1 receptor antagonist with very high BK B2 receptor antagonist activity, despite lacking a C-terminal Arg, displayed an ideal type II beta-turn from Pro2 to Igl5, as well as a salt bridge between the guanidino group of Arg1 and the carboXylate group of Oic8. B-10206, the most potent B2 antagonist, also displayed an ideal type II beta-turn from Pro2 to Igl5 but secondary structure was not observed at the C-terminal end. The third peptide, B-10284, a des-Arg9 analog with a C-terminal amide and a very potent B2 antagonist, had no definite solution structure. The high activity of these peptides emphasizes the importance of the N-terminal beta-turn and the hydrophobic character at the C-terminus in determining the activity of bradykinin antagonists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".