Altering Toluene 4-Monooxygenase by Active-Site Engineering for the Synthesis of 3-Methoxycatechol, Methoxyhydroquinone,
Bibliographic record
Abstract
Wild-type toluene 4-monooxygenase (T4MO) of Pseudomonas mendocina KR1 oxidizes toluene to p-cresol (96%) and oxidizes benzene sequentially to phenol, to catechol, and to 1,2,3-trihydroxybenzene. In this study T4MO was found to oxidize o-cresol to 3-methylcatechol (91%) and methylhydroquinone (9%), to oxidize m-cresol and p-cresol to 4-methylcatechol (100%), and to oxidize o-methoxyphenol to 4-methoxyresorcinol (87%), 3-methoxycatechol (11%), and methoxyhydroquinone (2%). Apparent Vmax values of 6.6 0.9 to 10.7 0.1 nmol/min/ mg of protein were obtained for o-, m-, and p-cresol oxidation by wild-type T4MO, which are comparable to the toluene oxidation rate (15.1 0.8 nmol/min/mg of protein). After these new reactions were discovered, satu-ration mutagenesis was performed near the diiron catalytic center at positions I100, G103, and A107 of the alpha subunit of the hydroxylase (TmoA) based on directed evolution of the related toluene o-monooxygenase of Burkholderia cepacia G4 (K. A. Canada, S. Iwashita, H. Shim, and T. K. Wood, J. Bacteriol. 184:344-349, 2002) and a previously reported T4MO G103L regiospecific mutant (K. H. Mitchell, J. M. Studts, and B. G. Fox, Biochemistry 41:3176-3188, 2002). By using o-cresol and o-methoxyphenol as model substrates, regiospe-cific mutants of T4MO were created; for example, TmoA variant G103A/A107S produced 3-methylcatechol (98%) from o-cresol twofold faster and produced 3-methoxycatechol (82%) from 1 mM o-methoxyphenol seven times faster than the wild-type T4MO (1.5 0.2 versus 0.21 0.01 nmol/min/mg of protein). Variant I100L produced 3-
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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 | 0.000 |
| 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".