Understanding epoxide hydrolase regiospecificity: towards the discovery and design of highly selective biocatalysts (LB133)
Bibliographic record
Abstract
The ever‐increasing quantity of genomic data continues to reveal a great deal about microbial natural product biosynthesis and has enabled new molecule and biocatalyst discovery. However, predicting chemical structures from gene sequences remains challenging because even enzymes with high sequence similarity may catalyze unexpectedly different reactions. This information gap between genes and chemical structure can be narrowed by careful comparison of related biosynthetic enzymes. For example, epoxide hydrolases (EHs) of very high sequence similarity from related enediyne biosynthetic pathways can have different regiospecificities, resulting in opposite stereochemical configuration in the products. Specifically, “inverting” EHs hydrolyze an (S)‐epoxide substrate to generate an (R)‐vicinal diol while “retaining” EHs yield an (S)‐vicinal diol. Intriguingly, the inverting EHs possess only one of the two canonical Tyr residues present in EHs. Biochemical characterization of several EH mutants suggests that Tyr substitution may direct EH regioselectivity. Indeed, genome mining has identified additional enediyne biosynthesis‐associated EHs that further the predictive utility of the Tyr substitution, thereby setting the stage for understanding 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".