Predictive Model for Epoxide Hydrolase-Generated Stereochemistry in the Biosynthesis of Nine-Membered Enediyne Antitumor Antibiotics
Bibliographic record
Abstract
Nine-membered enediyne antitumor antibiotics C-1027, neocarzinostatin (NCS), and kedarcidin (KED) possess enediyne cores to which activity-modulating peripheral moieties are attached via (R)- or (S)-vicinal diols. We have previously shown that this stereochemical difference arises from hydrolysis of epoxide precursors by epoxide hydrolases (EHs) with different regioselectivities. The inverting EHs, such as SgcF, hydrolyze an (S)-epoxide substrate to yield an (R)-diol in C-1027 biosynthesis, whereas the retaining EHs, such as NcsF2 and KedF, hydrolyze an (S)-epoxide substrate to yield an (S)-diol in NCS and KED biosynthesis. We now report the characterization of a series of EH mutants and provide a predictive model for EH regioselectivity in the biosynthesis of the nine-membered enediyne antitumor antibiotics. A W236Y mutation in SgcF increased the retaining activity toward (S)-styrene oxide by 3-fold, and a W236Y/Q237M double mutation in SgcF, mimicking NcsF2 and KedF, resulted in a 20-fold increase in the retaining activity. To test the predictive utility of these mutations, two putative enediyne biosynthesis-associated EHs were identified by genome mining and confirmed as inverting enzymes, SpoF from Salinospora tropica CNB-440 and SgrF (SGR_625) from Streptomyces griseus IFO 13350. Finally, phylogenetic analysis of EHs revealed a familial classification according to inverting versus retaining activity. Taken together, these results provide a predictive model for vicinal diol stereochemistry in enediyne biosynthesis and set the stage for further elucidating the origins of EH regioselectivity.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".