Cysteine Nucleophiles in Glycosidase Catalysis: Application of a Covalent β‐<scp>l‐</scp>Arabinofuranosidase Inhibitor
Bibliographic record
Abstract
(Glu) coordination complex, has presented unresolved mechanistic questions. In particular, the proposed mechanism, depending on a Zn-coordinated cysteine nucleophile and passing through a thioglycosyl enzyme intermediate, remains controversial. This is primarily due to the expected stability of the intermediate C-S bond. To facilitate the study of this atypical mechanism, we report the synthesis of a cyclophellitol-derived β-l-arabinofuranosidase inhibitor, hypothesised to react with the catalytic nucleophile to form a non-hydrolysable adduct analogous to the mechanistic covalent intermediate. This β-l-arabinofuranosidase inhibitor reacts exclusively with the proposed cysteine thiol catalytic nucleophiles of representatives of GH families 127 and 146. X-ray crystal structures determined for the resulting adducts enable MD and QM/MM simulations, which provide insight into the mechanism of thioglycosyl enzyme intermediate breakdown. Leveraging the unique chemistry of cyclophellitol derivatives, the structures and simulations presented here support the assignment of a zinc-coordinated cysteine as the catalytic nucleophile and illuminate the finely tuned energetics of this remarkable metalloenzyme clan.
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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 | 0.000 |
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".