Both Chemical and Non-Chemical Steps Limit the Catalytic Efficiency of Family 4 Glycoside Hydrolases
Bibliographic record
Abstract
The glycoside hydrolase family 4 (GH4) α-galactosidase from Citrobacter freundii (MelA) catalyzes the hydrolysis of fluoro-substituted phenyl α- d -galactopyranosides by utilizing two cofactors, NAD + and a metal cation, under reducing conditions. In order to refine the mechanistic understanding of this GH4 enzyme, leaving group effects were measured with various metal cations. The derived β lg value on V / K for strontium activation is indistinguishable from zero (0.05 ± 0.12). Deuterium kinetic isotope effects (KIEs) were measured for the activated substrates 2-fluorophenyl and 4-fluorophenyl α- d -galactopyranosides in the presence of Sr 2+, Y 3+, and Mn 2+, where the isotopic substitution was on the carbohydrate at C-2 and/or C-3. To determine the contributing factors to the virtual transition state (TS) on which the KIEs report, kinetic isotope effects on isotope effects were measured on these KIEs using doubly deuterated substrates. The measured D V / K KIEs for MelA-catalyzed hydrolysis of 2-fluorophenyl α- d -galactopyranoside are closer to unity than the measured effects on 4-fluorophenyl α- d -galactopyranoside, irrespective of the site of isotopic substitution and of the metal cation activator. These observations are consistent with hydride transfer at C-3 to the on-board NAD +, deprotonation at C-2, and a non-chemical step contributing to the virtual TS for V / K .
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".