Molecular Basis for the Stereoselective Ammoniolysis of <i>N</i>‐Alkyl Aziridine‐2‐Carboxylates Catalyzed by <i>Candida antarctica</i> Lipase B
Bibliographic record
Abstract
Candida antarctica lipase B catalyzed the stereoselective ammoniolysis of N-alkyl aziridine-2-carboxylates in tBuOH saturated with ammonia and yielded the (2S)-aziridine-2-carboxamide and unreacted (2R)-aziridine-2-carboxylate. Varying the N-1 substituent on the aziridine ring changed the rate and stereoselectivity of the reaction. Substrates with a benzyl substituent or a (1'R)-1-phenylethyl substituent reacted approximately ten times faster than substrates with a (1'S)-1-phenylethyl substituent. Substrates with a benzyl substituent showed little stereoselectivity (E=5-7) while substrates with either a (1'R)- or (1'S)-1-phenylethyl substituent showed high stereoselectivity (D>50). Molecular modeling by using the current paradigm for enantioselectivity-binding of the slow enantiomer by an exchange-of-substituents orientation-could not account for the experimental results. However, modeling an umbrella-like-inversion orientation for the slow enantiomer could account for the experimental results. Steric hindrance between the methyl in the (1'S)-1-phenylethyl substituent and Thr138 and Ile189 in the acyl-binding site likely accounts for the slow reaction. Enantioselectivity likely stems from an unfavorable interaction of the methine hydrogen with Thr40 for the slow enantiomer and from subtle differences in the orientations of the other three substituents. This success in rationalizing the enantioselectivity supports the notion that an umbrella-like-inversion orientation can contribute to enantioselectivity in lipases.
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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 | 0.000 |
| 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".