A thermostable laccase from Thermus sp. 2.9 and its potential for delignification of Eucalyptus biomass
Bibliographic record
Abstract
Laccases are multicopper oxidases that are being studied for their potential application in pretreatment strategies of lignocellulosic feedstocks for bioethanol production. Here, we report the expression and characterization of a predicted laccase (LAC_2.9) from the thermophilic bacterial strain Thermus sp. 2.9 and investigate its capacity to delignify lignocellulosic biomass. The purified enzyme displayed a blue color typical of laccases, showed strict copper dependence and retained 80% of its activity after 16 h at 70 °C. At 60 °C, the enzyme oxidized 2,2′-azino-di-(3-ethylbenzthiazoline sulfonate) (ABTS) and 2,6-dimethoxyphenol (DMP) at optimal pH of 5 and 6, respectively. LAC_2.9 had higher substrate specificity ( k cat / K M ) for DMP with a calculated value that accounts for one of the highest reported for laccases. Further, the enzyme oxidized a phenolic lignin model dimer. The incubation of steam-exploded eucalyptus biomass with LAC_2.9 and 1-hydroxybenzotriazole (HBT) as mediator changed the structural properties of the lignocellulose as evidenced by Fourier transform infrared (FTIR) spectroscopy and thermo-gravimetric analysis (TGA). However, this did not increase the yield of sugars released by enzymatic saccharification. In conclusion, LAC_2.9 is a thermostable laccase with potential application in the delignification of lignocellulosic biomass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".