Using Enzymes to Tame Nitrogen-Centered Radicals for Enantioselective Hydroamination
Bibliographic record
Abstract
The construction of C–N bonds is essential for the preparation of numerous molecules critical to modern society1,2. Nature has evolved enzymes to facilitate these transformations using nucleophilic and nitrene transfer mechanisms3,4. However, neither natural nor engineered enzymes are known to generate and control nitrogen-centered radicals, which serve as valuable species for C–N bond formation. Herein, we describe a platform for generating nitrogen-centered radicals within protein active sites, thus enabling asymmetric hydroamination reactions. Using flavin- dependent ‘ene’-reductases with an exogenous photoredox catalyst, amidyl radicals are generated selectively within the protein active site. Empowered by directed evolution, these enzymes are engineered to catalyze 5-exo, 6-endo, 7-endo, 8-endo, and intermolecular hydroamination reactions with high levels of enantioselectivity. Mechanistic studies suggest that radical initiation occurs via an enzyme-gated mechanism, where the protein thermodynamically activates the substrate for reduction by the photocatalyst. Molecular dynamics studies suggest that the enzymes bind substrates using non-canonical binding interactions, which may serve as a handle to further manipulate reactivity. This approach demonstrates the versatility of these enzymes for controlling the reactivity of high-energy radical intermediates and highlight the opportunity for synergistic catalyst strategies to unlock new enzymatic functions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".