<i>O</i>-GlcNAcase Catalyzes Cleavage of Thioglycosides without General Acid Catalysis
Bibliographic record
Abstract
O-GlcNAcase catalyzes the removal of N-acetylglucosamine residues from serine and threonine residues of post-translationally modified proteins using a catalytic mechanism involving substrate-assisted catalysis and general acid/base catalysis. Since thioglycosides are widely perceived as resistant to hydrolysis by glycosidases, it was surprising to find that O-GlcNAcase also catalyzes the efficient hydrolysis of S-glycosides. Brønsted analyses and pH-activity studies of the O-GlcNAcase-catalyzed hydrolysis of a series of aryl S- and O-glycosides reveal that O-GlcNAcase effects hydrolysis of thioglycosides without the assistance of general acid catalysis. alpha-Deuterium kinetic isotope effects for O- and S-glycosides, as well as Taft-like analyses using N-fluoroacetyl-beta-glycosides, suggest that O-GlcNAcase accomplishes hydrolysis of thioglycosides by stabilizing late transition states. For S-glycosides this transition state shows greater nucleophilic participation from the 2-acetamido group than for O-glycosides. The rate constants governing the O-GlcNAcase-catalyzed hydrolysis of O- and S-glycosides as compared to those previously determined for the spontaneous hydrolysis of structurally similar O,O- and O,S-acetals show a similar ratio. O-GlcNAcase therefore demonstrates similar catalytic proficiency toward both O- and S-glycosides. We conclude that O-GlcNAcase is a bifunctional catalyst capable of efficiently cleaving thioglycosides without general acid catalysis, an observation that may have biological implications.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".