A novel Bacillus ligniniphilus catechol 2,3-dioxygenase shows unique substrate preference and metal requirement
Bibliographic record
Abstract
Abstract Identification of novel enzymes from lignin degrading microorganisms will help to develop biotechnologies for biomass valorization and aromatic hydrocarbons degradation. Bacillus ligniniphilus L1 grows with alkaline lignin as the single carbon source and is a great candidate for ligninolytic enzyme identification. The first dioxygenase from strain L1 was heterologously expressed, purified, and characterized with an optimal temperature and pH of 32.5 °C and 7.4, respectively. It showed the highest activity with 3-ethylcatechol and significant activities with other substrates in the decreasing order of 3-ethylcatechol > 3-methylcatechol > 3-isopropyl catechol > 2, 3-dihydroxybiphenyl > 4-methylcatechol > catechol. It did not show activities against other tested substrates with similar structures. Most reported catechol 2,3-dioxygenases (C23Os) are Fe2+-dependent whereas Bacillus ligniniphilus catechol 2,3-dioxygenase (BLC23O) is more Mn2+- dependent. At 1 mM, Mn2+ led to 230-fold activity increase and Fe2+ led to 22-fold increase. Sequence comparison and phylogenetic analyses suggested that BL23O is different from other Mn-dependent enzymes and uniquely grouped with an uncharacterized vicinal oxygen chelate (VOC) family protein from Paenibacillus apiaries. Gel filtration analysis showed that BLC23O is a monomer under native condition. This is the first report of a C23O from Bacillus ligniniphilus L1 with unique substrate preference, metal-dependency, and monomeric structure.
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) | 0.000 | 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 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".