Kinetic differences of purified laccases from six Pleurotus ostreatus strains
Bibliographic record
Abstract
AIMS: Enzyme kinetics of purified laccases from six different Pleurotus ostreatus strains were determined in the oxidation of syringaldazine, guaiacol and ABTS. METHODS AND RESULTS: Significant differences in the kinetic constants were found. Catalytic activity (kcat) ranged from 19 to 941 U mg(-1) for syringaldazine, from 18 to 1565 U mg(-1) for ABTS, and from 4 to 44 U mg(-1) for guaiacol. The apparent affinity constants (KM) also showed significant differences between the different strains, from 12 to 52 micromol l(-1) for syringaldazine, from 8 to 79 micromol l(-1) for ABTS, and from 0.46 to 6.61 mmol l(-1) for guaiacol. No differences were found either on the effect of increasing concentrations of organic solvent (acetonitrile) or on the activity pH profile. The temperature profile was the same for all the P. ostreatus strains, except for the IE8 strain, which seems to be more sensitive to temperature. The kinetic and stability data from the six P. ostreatus strains were also compared with those obtained from other white rot fungi, Coriolopsis gallica and Trametes versicolor, showing clear differences. CONCLUSION: The different P. ostreatus isolates showed different kinetic constants. SIGNIFICANCE AND IMPACT OF THE STUDY: The different enzymatic properties of laccases from various P. ostreatus strains should be considered for a potential industrial or environmental application.
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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) | 0.000 | 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".