A Mechanistic Study on the Non‐enzymatic Hydrolysis of Kdn Glycosides
Bibliographic record
Abstract
Abstract Sialic acids are biologically important carbohydrates that are prevalent throughout nature. We are interested in their intrinsic reactivity in aqueous solution and how such reactivity affects the design of substrates for investigation of enzymes that process these sugars. To probe the reactivity differences between two sialic acid family members N‐acetylneuraminic acid and Kdn we measured the rate constants for hydrolysis of 4‐nitrophenyl 3‐deoxy‐d‐glycero‐α‐d‐galacto‐non‐2‐ulosonide in aqueous solution. The kinetic data is consistent with glycosidic C−O bond cleavage occurring via four mechanistic pathways, and these are: (i) hydronium ion‐catalyzed hydrolysis of the neutral sugar; (ii) hydronium ion‐catalyzed hydrolysis of the glycosidic carboxylate; (iii) water‐catalyzed hydrolysis of the anionic glycoside; and (iv) base‐promoted reaction of the anionic glycoside. To study the effects of C‐5 substitution on the Kdn glycoside we made 4‐nitrophenyl 5‐O‐methyl‐α‐Kdn glycoside and determined its rate constants for hydrolysis. All hydrolytic rate constants for both Kdn glycosides were larger than those reported for the parent N‐acetyl‐α‐neuraminide. The water‐catalyzed reaction (pathway iii) exhibited a βlg value of −1.3±0.1. We conclude that the larger rate constants associated with C5‐oxygen containing sialosides results from less steric congestion at the hydrolytic transition states than for the parent C‐5 acetamido glycoside.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".