Sequence Similarity Network Guided Discovery of a Dehydrogenase for Asymmetric Carbonyl Dehydrogenation
Bibliographic record
Abstract
Abstract Carbonyl dehydrogenation is one of the most valuable transformations in modern synthetic chemistry. As compared to traditional chemical synthesis methods, enzymatic dehydrogenation offers a greener and more selective alternative. However, except for a few rare natural dehydrogenases for desaturation, current enzymatic methods predominantly rely on enzyme promiscuity, which often suffers from lower efficiency and limited reaction controllability. Herein, we employed sequence similarity networks to mine natural dehydrogenases from a vast array of sequences with potential dehydrogenation activity. This approach led to the discovery of an uncharacterized FAD‐dependent enzyme capable of efficiently performing the desymmetrizing desaturation of cyclohexanones, thereby generating diverse cyclohexenones bearing remote γ‐quaternary stereocenters. The current method has enhanced the turnover frequency (TOF) by approximately 178‐fold as compared to the best existing biocatalytic strategies and displayed almost no overoxidation reactions. Through a combination of experimental assays and computational studies, we elucidated that this enzyme enhances its dehydrogenation capability through an unconventional proton relay system, absent in previously reported enzyme promiscuity systems. Additionally, this streamlined enzymatic process demonstrated scalability to gram‐scale synthesis with maintained efficiency and selectivity, offering robust and sustainable alternatives for the synthesis of chiral cyclohexenones with high optical purity.
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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 | 0.000 |
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".