Engineering of a thioglycoligase: randomized mutagenesis of the acid-base residue leads to the identification of improved catalysts
Bibliographic record
Abstract
Thioglycoligases are recently introduced variants of retaining glycosidases in which the acid-base catalyst has been mutated, rendering them capable of thioglycoside synthesis. The original acid-base mutant of Agrobacterium sp. beta-glucosidase (E170A) was previously shown to be an effective thioglycoligase carrying out glycosyltransfer from 2,4-dinitrophenyl glycosides to several different thio sugar acceptors. Here we report the generation of a screen for improved thioglycoligases, randomized mutagenesis of the acid-base catalyst E170 and identification of variants superior to E170A. Furthermore we have established a coupled assay allowing kinetic analysis of isolated variants and found that Abg E170Q is 5-fold faster than Abg E170A when 2,4-dinitrophenyl glucoside is used as donor and 100-fold faster when glucosyl azide is used. To demonstrate its utility, different acceptor and donor sugar combinations were employed to produce thio-linked di- or trisaccharides in high yields, showing the considerable versatility of the system for the synthesis of carbohydrate mimetics.
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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) | 0.001 | 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 | 0.000 |
| 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".