Enzymatic Synthesis of Galactosylated Serine/Threonine Derivatives by β-Galactosidase from Escherichia coli
Bibliographic record
Abstract
The transgalactosylations of serine/threonine derivatives were investigated using β-galactosidase from Escherichia coli as biocatalyst. Using ortho-nitrophenyl-β-D-galactoside as donor, the highest bioconversion yield of transgalactosylated N-carboxy benzyl L-serine benzyl ester (23.2%) was achieved in heptane:buffer medium (70:30), whereas with the lactose, the highest bioconversion yield (3.94%) was obtained in the buffer reaction system. The structures of most abundant galactosylated serine products were characterized by MS/MS. The molecular docking simulation revealed that the binding of serine/threonine derivatives to the enzyme's active site was stronger (-4.6~-7.9 kcal/mol) than that of the natural acceptor, glucose, and mainly occurred through interactions with aromatic residues. For N-tert-butoxycarbonyl serine methyl ester (6.8%) and N-carboxybenzyl serine benzyl ester (3.4%), their binding affinities and the distances between their hydroxyl side chain and the 1'-OH group of galactose moiety were in good accordance with the quantified bioconversion yields. Despite its lower predicted bioconversion yield, the high experimental bioconversion yield obtained with N-carboxybenzyl serine methyl ester (23.2%) demonstrated the importance of the thermodynamically-driven nature of the transgalactosylation reaction.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".