Use of conformationally restricted pyridinium α-D-<i>N</i>-acetylneuraminides to probe specificity in bacterial and viral sialidases
Bibliographic record
Abstract
Investigations into subtle changes in the catalytic activity of sialidases have been performed using enzymes from several different origins, and their results have been compared. This work highlights the potential pitfalls encountered when extending conclusions derived from mechanistic studies on a single enzyme even to those with high-sequence homology. Specifically, a panel of 5 pyridinium N-acetylneuraminides were used as substrates in a study that revealed subtle differences in the catalytic mechanisms used by 4 different sialidase enzymes. The lowest reactivity towards the artificial (pyridinium) substrates was displayed by the Newcastle disease virus hemagglutinin-neuraminidase. Moreover, in reactions involving aryl N-acetylneuraminides, the activity of the Newcastle enzyme was competitively inhibited by the 3,4-dihydro-2H-pyrano[3,2-c]pyridinium compound with a Ki = 58 micromol/L. Alternatively, the 3 bacterial enzymes tested, from Salmonella typhimurium, Clostridium perfringens, and Vibrio cholerae, were catalytically active against all members of the panel of substrates. Based on the observed effect of leaving-group ability, it is proposed that the rate-determining step for kcat (and likely for kcat/Km as well) with each bacterial enzyme is as follows: sialylation, which is concerted with conformational change for V. cholerae; and conformational change for S. typhimurium and C. perfringens.
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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".