Versatile biocatalyst: lipase from Streptomyces gobitricini for ester synthesis and detergent innovation
Bibliographic record
Abstract
Background/objectives The growing demand for reliable and stable biocatalysts has spurred research into microbial lipases for diverse industrial applications. This study focused on enhancing the production and purification of a lipase from Streptomyces gobitricini (Lip S.g ). Methods Maximal lipase activity (420 U/mL) was achieved during the stationary phase after 84 h of incubation at 45°C and pH 8.0, using 2% glucose and 2% yeast extract as carbon and nitrogen sources, respectively. Results Calcium, olive oil, and Tween, at 1%, significantly enhanced Lip S.g production, highlighting the role of triglycerides and detergents in enzyme induction and substrate emulsification. The purified 50-kDa enzyme displayed maximal activity at 50°C and pH 9.0, with thermal stability between 40°C and 55°C and pH 5.0–10.0. While Lip S.g efficiently hydrolyzed short and medium-chain triglycerides, it exhibited a preference for long-chain substrates, with a maximum reaction rate of 2500 μmol/min/mg and a K m value of 6.45 mM toward triolein (C18). Lip S.g also demonstrated remarkable stability in detergent formulations, retaining more than 85% activity in the presence of surfactants, oxidizing agents, boron compounds, and enzyme inhibitors. Additionally, Lip S.g catalyzed the esterification of oleic acid with starch and ethanol to produce starch oleate and ricinoleic acid. Conclusion These findings establish Lip S.g as a promising biocatalyst for applications in biocatalysis and detergent formulations, with potential uses in the food, beverage, cosmetic, and pharmaceutical industries.
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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".