Engineering the NADPH specificity of DepB, a novel aldo-keto reductase involved in the detoxification of the agroeconomic mycotoxin deoxynivalenol (DON)
Bibliographic record
Abstract
Deoxynivalenol (DON) is a toxic fungal secondary metabolite produced by Fusarium graminearum which causes Fusarium Head Blight and Pink Ear Rot disease in wheat and corn respectively. DON is a predominant contaminant in cereal grain crops with outbreaks costing the North American cereal grain industry millions of dollars annually. There is a growing need for effective DON mitigation strategies due to DON’s inherent toxicity which affects the performance of livestock fed contaminated grain. Current DON management strategies involve physical decontamination or marginally effective chemical treatments; however, a holistic and targeted approach via the incorporation of DON detoxifying enzymes is a promising strategy. Previous studies demonstrated that D. mutans 17-2-E-8, a soil bacterium, epimerizes DON to the less toxic 3-epi-DON via the intermediate, 3-keto-DON. The process involves two enzymes, DepA, a PQQ-dependent dehydrogenase, and DepB, an NADPH-dependent aldo-keto reductase (AKR). The strict requirement for the expensive cofactor, NADPH, poses a significant impediment to the practical application of these enzymes. Protein engineering approaches can address this issue – by ‘switching’ DepB’s cofactor preference to the cheaper co-factor, NADH. DepB was found to catalyze the transformation of 3-keto DON to 3-epi DON with Km and kcat values of 563.9 µM and 2.49s-1, respectively, using NADPH as a cofactor. Secondly, the enzyme’s Kd for NADPH was determined to be 44.23 µM using fluorescence enhancement assays. Using the solved crystal structure of DepB, docking experiments with DepB revealed that Arg-289, Gln-293, and Lys-216 may be important for NADPH specificity. Therefore, site-specific mutagenesis was performed to replace these residues to enable the enzyme to utilize NADH. The catalytic efficiencies for these designed mutants will next be determined and compared to catalytic efficiencies of the wild type DepB.
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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) | 0.001 | 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".