An efficient C-glycoside production platform enabled by rationally tuning the chemoselectivity of glycosyltransferases
Bibliographic record
Abstract
Despite the broad potential applications of C-glycosides, facile synthetic methods remain scarce. Transforming glycosyltransferases with promiscuous or natural O-specific chemoselectivity to C-glycosyltransferases is challenging. Here, we employ rational directed evolution of the glycosyltransferase MiCGT to generate MiCGT-QDP and MiCGT-ATD mutants which either enhance C-glycosylation or switch to O-glycosylation, respectively. Structural analysis and computational simulations reveal that substrate binding mode govern C-/O-glycosylation selectivity. Notably, directed evolution and mechanism analysis pinpoint the crucial residues dictating the binding mode, enabling the rational design of four enzymes with superior non-inherent chemoselectivity, despite limited sequence homology. Moreover, our best mutants undergo testing with 34 substrates, demonstrating superb chemoselectivities, regioselectivities, and activities. Remarkably, three C-glycosides and an O-glycoside are produced on a gram scale, demonstrating practical utility. This work establishes a highly selective platform for diverse glycosides, and offers a practical strategy for creating various types of glycosylation platforms to access pharmaceutically and medicinally interesting products. Despite the potential utility of C-glycosides, synthetic routes for their synthesis are limited. Here, the authors used rational directed evolution of the glycosyltransferase MiCGT to generate MiCGT-QDP and MiCGT-ATD mutants which either enhance C-glycosylation or switch to O-glycosylation, respectively, and reveal the substrate binding mode that governs C-/O-glycosylation 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) | 0.000 | 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".