Efficient NADPH-dependent dehalogenation afforded by a self-sufficient reductive dehalogenase
Bibliographic record
Abstract
Reductive dehalogenases are corrinoid and iron-sulfur cluster-containing enzymes that catalyze the reductive removal of a halogen atom. The oxygen-sensitive and membrane-associated nature of the respiratory reductive dehalogenases has hindered their detailed kinetic study. In contrast, the evolutionarily related catabolic reductive dehalogenases are oxygen tolerant, with those that are naturally fused to a reductase domain with similarity to phthalate dioxygenase presenting attractive targets for further study. We present efficient heterologous expression of a self-sufficient catabolic reductive dehalogenase from Jhaorihella thermophila in Escherichia coli. Combining the use of maltose-binding protein as a solubility-enhancing tag with the btuCEDFB cobalamin uptake system affords up to 40% cobalamin occupancy and a full complement of iron-sulfur clusters. The enzyme is able to efficiently perform NADPH-dependent dehalogenation of brominated and iodinated phenolic compounds, including the flame retardant tetrabromobisphenol, under both anaerobic and aerobic conditions. NADPH consumption is tightly coupled to product formation. Surprisingly, corresponding chlorinated compounds only act as competitive inhibitors. Electron paramagnetic resonance spectroscopy reveals loss of the Co(II) signal observed in the resting state of the enzyme under steady-state conditions, suggesting accumulation of Co(I)/(III) species prior to the rate-limiting step. In vivo reductive debromination activity is readily observed, and when the enzyme is expressed in E. coli strain W, supports growth on 3-bromo-4-hydroxyphenylacetic as a sole carbon source. This demonstrates the potential for catabolic reductive dehalogenases for future application in bioremediation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".