Immobilization and stabilization of levansucrase biocatalyst of high interest for the production of fructooligosaccharides and levan
Bibliographic record
Abstract
Abstract BACKGROUND Levansucrase (LS)‐catalyzed‐transfructosylation reaction is a potential approach for the synthesis of fructooligosaccharides (FOSs) and levan as health promoting compounds. This biocatalytic approach is hindered by low thermal stability of LS and its high rate of hydrolysis. In the present study, LS from Bacillus amyloliquefaciens was immobilized onto modified and unmodified epoxy‐activated supports (Eupergit® C; Sepabeads®) and on modified cross‐linked‐agarose beads, to increase its thermal stability and modulate its reaction selectivity (hydrolysis/transfructosylation). RESULTS LS bound to Sepabeads® HA (98.8%) and glyoxyl agarose‐IDA/Cu (67%) retained high initial activity along with good immobilization yields. The thermal stability results indicated that glyoxyl agarose‐IDA/Cu and glyoxyl agarose, provided the greatest thermal stability with factors of 14 and 106 times, respectively. Immobilization through Sepabeads® HA increased the ratio of transfructosylation/hydrolysis by 2.3 times, although it did not promote the stabilization of LS. Immobilization on glyoxyl agarose‐IDA/Cu provided a good compromise of all three properties: retention of activity (67.0%), transfructosylation/hydrolysis ratio (120%) and thermal stability (stability factor of 13.6). CONCLUSION The stabilization of LS through immobilization contributes to its potential use commercially. With an increasingly stable enzyme, further work will be directed towards altering LS reaction specificity towards levan and levan‐type FOSs. © 2015 Society of Chemical Industry
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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) | 0.000 | 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".