Rigid Dipeptide Surrogates: Syntheses of Enantiopure Quinolizidinone and Pyrroloazepinone Amino Acids from a Common Diaminodicarboxylate Precursor
Bibliographic record
Abstract
A versatile and practical approach for synthesizing azabicyclo[X.Y.0]alkane amino acids of different ring sizes from a common diaminodicarboxylate precursor has been developed as a means for mimicking different peptide conformations. (2S,9S)-1-tert-Butyl 10-benzyl 5-oxo-2-[N-(PhF)amino] 9-[N-(BOC)amino]dec-4-enedioate (18) was first prepared in 83% yield by the Horner-Wadsworth-Emmons olefination of N-(PhF)aspartate beta-aldehyde 8 with pyroglutamate-derived beta-keto phosphonate 12 (PhF = 9-phenylfluoren-9-yl). The practicality of this approach for making azabicyclo[X.Y.0]alkane amino acids was then illustrated by the first synthesis of enantiopure quinolizidin-2-one amino acid 6 in seven steps and 40% overall yield from L-pyroglutamic acid. Hydrogenation of delta-keto alpha,omega-diaminosebacate 18, followed by lactam cyclization and protection, gave quinolizidin-2-one amino acid 6 as a single diastereomer. The versatility of this approach was next demonstrated by the synthesis of both ring-fusion isomers of pyrroloazepin-2-one amino acid 6 in 11 steps and 13% overall yield from pyroglutamic acid. Hydride reduction of 18, followed by methanesulfonate displacement, gave 5-alkylproline 22. Protective group manipulations, lactam cyclization, and removal of the ester group afforded readily separable pyrroloazepinone amino acids (7S)- and (7R)-7 in a 1:2 diastereomeric ratio. By introducing two new azabicycloalkane amino acids using our olefination approach, we have expanded the diversity of these important heterocycles for studying the conformational requirements for peptide biological activity.
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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 | 0.001 |
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".