Role of Metal Ions, Surfactants and Solvents on Enzymatic Activity of Partial Purified Glucoamylase from Aspergillus niger ER05
Bibliographic record
Abstract
The glucoamylase requirement of different industries should vary in their physiochemical and functional properties, so the investigation of new sources for the novel enzymes is the only solution. The current study describe the production of glucoamylase from Aspergillus niger ER05 in a submerged fermentation. The enzyme then partially purified and then effect of thirteen different metal ions (Cs+, K+, Na+, Ba2+, Ca2+ Co2+, Cu2+ , Hg2+, Mg2+, Mn2+, Ni2+, Zn2+ , Fe3+), surfactant as well as organic solvent on catalytic performance of glucoamylase was studied. A newly isolated Aspergillus niger ER05 is hyper producing strain of glucoamylase. Specific activity for the crude enzyme was found to be 6.87 KU/mg. The crude enzyme was partially purified via fractional ammonium sulphate precipitation. Ammonium sulphate saturation from 40-80% was found suitable to precipitate the enzyme. After dialyzing precipitates, the specific activities were found to be 66.33KU/mg with percent yield of 26.5. The inhibitory influence of all metal studies was interestingly found on glucoamylase activity. The strong inhibition was demonstrated in presence of Hg2+, Ni2+, Zn2+, Cu2+, Fe3+. Only Na+ ions were able to maintain the 101% relative activity at 1mM concentration. The SDS completely inhibits the enzyme activity and in presence of tween-80 and triton X-100 glucoamylase exhibited less than 45% relative activity. Furthermore, formaldehyde, isopropanol, ethanol, methanol, and DMSO stabilized the enzyme activity while chloroform inhibits enzyme activity by 48%.
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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".