Influence of Metal ions, Surfactants and Organic Solvents on the Catalytic Performance of Levansucrase from Zymomonas mobilis KIBGE-IB14
Bibliographic record
Abstract
A significant progress has been made in discovering and developing new bacterial polysaccharides producing enzymes possessing extremely functional properties. Levan is a natural polymer of fructose linked by β (2→6) glycosidic bond which is produced by transfructosylation reaction in the presence of levansucrase. Among wide range of microorganisms, Zymomonas mobilis is considered as the most promising candidate for the production of extracellular levansucrase. It has potential applications in multiple industries from pharmaceutics, cosmetics to food industries. Determination of levansucrase characteristics is necessary to increase its industrial applications. This concept has directed much interest towards enzyme characterization by observing its effects against different chemicals. The present investigation focused on the characterization of levansucrase by observing its behavior with reference to different metal ions, surfactants and organic solvents. The results showed that these chemicals acted as activators, inhibitors or stabilizers. In metal ions, different activators (K+, Na+, Cs+, Ba+2, Ca+2, Cu+2, Mg+2 and Mn+2 ) and inhibitors (Co+2, Hg+2 , Fe+3and Al+3) were investigated. Among them, Hg+2 found to be strong inhibitor as it inhibits enzyme activity by 92% at 1 mM. Non-ionic surfactants i.e. triton X-100, tween-20 and tween-80 considered as stabilizers while anionic surfactant such as sodium dodecyl sulphate (SDS) inhibited the enzyme activity by 11%. Moreover, ethanol and methanol stabilized the enzyme activity while other solvents observed as inhibitors or stimulators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".