Engineering enzyme substrate scope complementarity for promiscuous cascade synthesis
Bibliographic record
Abstract
Biocatalytic cascades are uniquely powerful for the efficient, asymmetric synthesis of bioactive compounds. The high specificity of enzymes can enable one-pot reactions where the substrates, intermediates, and products react only with the intended enzyme. However, this same specificity can hinder the substrate scope of biocatalytic cascades because each constituent enzyme requires complementary activity. Here, we implement a substrate multiplexed screening (SUMS) approach to improve the substrate scope overlap of a two-enzyme cascade via directed evolution. This cascade leverages an L-threonine transaldolase, ObiH, to produce a range of β-OH amino acids that are subsequently decarboxylated to produce chiral 1,2-amino alcohols. Crucially, for the success of this cascade, we engineered a tryptophan decarboxylase to act efficiently on β-OH amino acids while avoiding activity on L-threonine, which is needed for ObiH activity. We leverage this exquisite selectivity with matched substrate scopes to produce a variety of chiral 1,2-amino alcohols in a one-pot cascade from aldehydes or styrene oxides. This route constitutes a new disconnection for the synthesis of β-adrenergic receptor agonists and shows how SUMS can be used to guide the development of promiscuous, C-C bond forming cascades.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".