Semi-rational approach for converting a GH1 -glycosidase into a -transglycosidase
Bibliographic record
Abstract
A large number of retaining glycosidases catalyze both hydrolysis and transglycosylation reactions, but little is known about what determines the balance between these two activities (transglycosylation/hydrolysis ratio). We previously obtained by directed evolution the mutants F401S and N282T of Thermus thermophilus β-glycosidase (Ttβ-gly, glycoside hydrolase family 1 (GH1)), which display a higher transglycosylation/hydrolysis ratio than the wild-type enzyme. In order to find the cause of these activity modifications, and thereby set up a generic method for easily obtaining transglycosidases from glycosidases, we determined their X-ray structure. No major structural changes could be observed which could help to rationalize the mutagenesis of glycosidases into transglycosidases. However, as these mutations are highly conserved in GH1 β-glycosidases and are located around the -1 site, we pursued the isolation of new transglycosidases by targeting highly conserved amino acids located around the active site. Thus, by single-point mutagenesis on Ttβ-gly, we created four new mutants that exhibit improved synthetic activity, producing disaccharides in yields of 68-90% against only 36% when native Ttβ-gly was used. As all of the chosen positions were well conserved among GH1 enzymes, this approach is most probably a general route to convert GH1 glycosidases into transglycosidases.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".