Chemoenzymatic macrocycle synthesis using resorcylic acid lactone thioesterase domains
Bibliographic record
Abstract
A key missing tool in the chemist's toolbox is an effective biocatalyst for macrocyclization. Macrocycles limit the conformational flexibility of small molecules, often improving their ability to bind selectively and with high affinity to a target, making them a privileged structure in drug discovery. Macrocyclic natural product biosynthesis offers an obvious starting point for biocatalyst discovery via the native macrocycle forming biosynthetic mechanism. Herein we demonstrate that the thioesterase domains (TEs) responsible for macrocyclization of resorcylic acid lactones are promising catalysts for the chemoenzymatic synthesis of 12- to 18-member ring macrolactones and macrolactams. The TE domains responsible for zearalenone and radicicol biosynthesis successfully generate resorcylate-like 12- to 18-member macrolactones and a 14-member macrolactam. In addition these enzymes can also macrolactonize a non-resorcylate containing depsipeptide, suggesting they are versatile biocatalysts. Simple saturated omega-hydroxy acyl chains are not macrocyclized, nor are the alpha-beta unsaturated derivatives, clearly outlining the scope of the substrate tolerance. These data dramatically expand our understanding of substrate tolerance of these enzymes and are consistent with our understanding of the role of TEs in iterative polyketide biosynthesis. In addition this work shows these TEs to be the most substrate tolerant polyketide macrocyclizing enzymes known, accessing resorcylate lactone and lactams as well as cyclicdepsipeptides, which are highly biologically relevant frameworks.
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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".