Regioselective synthesis of feruloylated glycosides using the feruloyl esterases expressed in selected commercial multi-enzymatic preparations as biocatalysts
Bibliographic record
Abstract
Twenty multi-enzymatic preparations were evaluated for their levels of feruloyl esterase activity and their substrate specificity using methyl ferulate and isolated feruloylated non-digestible oligosaccharides from sugar-beet pulp and wheat bran as substrates. The efficiency of feruloyl esterases expressed in the six best multi-enzymatic preparations from Bacillus spp. (Ceremix), Humicola insolens (Depol 740), Aspergillus oryzae (Flavourzyme), Bacillus amyloliquefaciens (Multifect P 3000), Bacillus subtilis (RP-1) and Trichoderma reesei (Depol 670) for the synthesis of feruloylated glycosides was investigated using a surfactant-less organic microemulsion system as reaction medium. Using n-hexane, 1-butanol and MES (3-(N-morpholino)ethanesulfonic acid)–NaOH buffer reaction mixture (51:46:3 v/v/v), feruloyl esterases expressed in Multifect P 3000 resulted in the highest bioconversion yield of feruloylated arabinose (37%), whereas those present in Depol 670 led to the highest bioconversion yield of feruloylated galactose (61%). The highest bioconversion yield of feruloylated xylose (37%) was obtained using the feruloyl esterase of Depol 670 as biocatalyst in n-hexane, 2-butanone and MES–NaOH buffer reaction mixture (51:46:3 v/v/v). Feruloylated arabinose, galactose and xylose demonstrated potential antioxidant properties as indicated by their profound radical scavenging activity. The chemical structure of the feruloylated glycosides was confirmed by APCI-MS.
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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 | 0.001 |
| 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".