Regioselective Radical Alkylation of Arenes Using Evolved Photoenzymes
Bibliographic record
Abstract
Abstract Substituted arenes are ubiquitous in molecules with medicinal functions, making their synthesis a critical consideration when designing synthetic routes.1,2 Regioselective C–H functionalization reactions are attractive for preparing alkylated arenes,3-5 however, the selectivity of existing methods is modest and primarily governed by substrate electronic properties.6,7 Here, we demonstrate a biocatalyst-controlled method for the regioselective alkylation of electron-rich and electron-deficient heteroarenes. Starting from an unselective ‘ene’-reductase (ERED) (GluER-T36A), we evolved a variant that selectively alkylates the C4 position of indole, an elusive position using prior technologies. Mechanistic studies across the evolutionary series indicate that changes to the protein active site alter the electronic character of the charge transfer (CT) complex responsible for radical formation. This resulted in a variant with a significant degree of ground state change transfer in the CT complex. Mechanistic studies on a C2 selective ERED suggest that the evolution of GluER-T36A helps disfavor a competing mechanistic pathway. Additional protein engineering campaigns were carried out for a C8 selective quinoline alkylation. This study highlights the opportunity to use enzymes for regioselective reactions where small molecule catalysts struggle to alter selectivity.
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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 | 0.001 |
| 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".