Engineering the enzyme toolbox to tailor glycosylation in small molecule natural products and protein biologics
Bibliographic record
Abstract
Many glycosylated small molecule natural products and glycoprotein biologics are important in a broad range of therapeutic and industrial applications. The sugar moieties that decorate these compounds often show a profound impact on their biological functions, thus biocatalytic methods for controlling their glycosylation are valuable. Enzymes from nature are useful tools to tailor bioproduct glycosylation but these sometimes have limitations in their catalytic efficiency, substrate specificity, regiospecificity, stereospecificity, or stability. Enzyme engineering strategies such as directed evolution or semi-rational and rational design have addressed some of the challenges presented by these limitations. In this review, we highlight some of the recent research on engineering enzymes to tailor the glycosylation of small molecule natural products (including alkaloids, terpenoids, polyketides, and peptides), as well as the glycosylation of protein biologics (including hormones, enzyme-replacement therapies, enzyme inhibitors, vaccines, and antibodies).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".