Benzophenone semicarbazone protection strategy for synthesis of aza‐glycine containing aza‐peptides
Bibliographic record
Abstract
Abstract Aza‐glycine has been incorporated into peptide mimics as a tool for studying the active conformation and characterizing structure‐function relationships for activity. Side reactions, such as intramolecular cyclizations to form hydantoins and oxadiazalones, have, however, inhibited efforts to make activated aza‐Gly residues in solution using carbamate protection. Herein, we describe efficient incorporation of aza‐glycine into aza‐peptides using diphenyl hydrazone protection. Hydrazone acylation with p‐nitrobenzyl chloroformate provided the protected aza‐Gly activated ester, which was used to acylate a set of amino ester and amino acids to provide aza‐Gly‐Xaa aza‐dipeptide fragments for peptide synthesis. Removal of the hydrazone protection was performed under acidic conditions to provide the hydrochloride salt of the aza‐Gly residue for subsequent elongation of the aza‐peptide chain using standard coupling conditions. A proof of concept for the use of benzophenone protection has been established by the synthesis of an aza‐peptide analog of a potent activator of caspase 9 in cancer cells. © 2008 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 90: 824–831, 2008. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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) | 0.001 | 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 | 0.001 |
| 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".