Macrocyclization strategies for the total synthesis of cyclic depsipeptides
Bibliographic record
Abstract
Cyclic depsipeptides are an important class of peptide natural products that are defined by the presence of ester and amide bonds within the macrocycle. The structural diversity of depsipeptides has required the development of a broad range of synthetic strategies to access these biologically active compounds. Solid phase peptide synthesis (SPPS) strategies have been an invaluable tool in their synthesis. The key aspect of their synthesis is the macrocyclization strategy. Three main strategies are used, solution phase macrolactamization of acyclic ester containing peptide, on-resin macrolactamization of a sidechain-anchored peptide, and the solution phase macrolactonization of a linear peptide. Additionally, biocatalysts have been used to produce these compounds in a regio- and chemo-selective manner. Each compound offers unique challenges, requiring careful synthetic design to avoid undesirable side reactivity or unwanted epimerization during the esterification and macrocyclizing steps. This focused review analyzes these three strategies for cyclic depsipeptide natural product total synthesis with selected examples from the literature between 2001-2023.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".