Investigating the Potential of Phenolic Compounds and Carbohydrates as Acceptor Substrates for Levansucrase‐Catalyzed Transfructosylation Reaction**
Bibliographic record
Abstract
This study characterizes the acceptor specificity of levansucrases (LSs) from Gluconobacter oxydans (LS1), Vibrio natriegens (LS2), Novosphingobium aromaticivorans (LS3), and Paraburkholderia graminis (LS4) using sucrose as fructosyl donor and selected phenolic compounds and carbohydrates as acceptors. Overall, V. natriegens LS2 proved to be the best biocatalyst for the transfructosylation of phenolic compounds. More than one fructosyl unit could be attached to fructosylated phenolic compounds. The transfructosylation of epicatechin by P. graminis LS4 resulted in the most diversified products, with up to five fructosyl units transferred. In addition to the LS source, the acceptor specificity of LS towards phenolic compounds and their transfructosylation products were found to greatly depend on their chemical structure: the number of phenolic rings, the reactivity of hydroxyl groups and the presence of aliphatic chains or methoxy groups. Similarly, for carbohydrates, the transfructosylation yield was dependent on both the LS source and the acceptor type. The highest yield of fructosylated-trisaccharides was Erlose from the transfructosylation of maltose catalyzed by LS2, with production reaching 200 g/L. LS2 was more selective towards the transfructosylation of phenolic compounds and carbohydrates, while reactions catalyzed by LS1, LS3 and LS4 also produced fructooligosaccharides. This study shows the high potential for the application of LSs in the glycosylation of phenolic compounds and carbohydrates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".