Artificial metalloenzymes based on biotin-avidin technology for the enantioselective reduction of ketones by transfer hydrogenation
Bibliographic record
Abstract
Most physiological and biotechnological processes rely on molecular recognition between chiral (handed) molecules. Manmade homogeneous catalysts and enzymes offer complementary means for producing enantiopure (single-handed) compounds. As the subtle details that govern chiral discrimination are difficult to predict, improving the performance of such catalysts often relies on trial-and-error procedures. Homogeneous catalysts are optimized by chemical modification of the chiral environment around the metal center. Enzymes can be improved by modification of gene encoding the protein. Incorporation of a biotinylated organometallic catalyst into a host protein (avidin or streptavidin) affords versatile artificial metalloenzymes for the reduction of ketones by transfer hydrogenation. The boric acid.formate mixture was identified as a hydrogen source compatible with these artificial metalloenzymes. A combined chemo-genetic procedure allows us to optimize the activity and selectivity of these hybrid catalysts: up to 94% (R) enantiomeric excess for the reduction of p-methylacetophenone. These artificial metalloenzymes display features reminiscent of both homogeneous catalysts and enzymes.
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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 | 0.001 | 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".