Exploring the biochemical properties and remediation applications of the unusual explosive-degrading P450 system XplA/B
Bibliographic record
Abstract
Widespread contamination of land and groundwater has resulted from the use, manufacture, and storage of the military explosive hexa-hydro-1,3,5-trinitro-1,3,5-triazine (RDX). This contamination has led to a requirement for a sustainable, low-cost method to remediate this problem. Here, we present the characterization of an unusual microbial P450 system able to degrade RDX, consisting of flavodoxin reductase XplB and fused flavodoxin-cytochrome P450 XplA. The affinity of XplA for the xenobiotic compound RDX is high (K(d) = 58 muM) and comparable with the K(m) of other P450s toward their natural substrates (ranging from 1 to 500 muM). The maximum turnover (k(cat)) is 4.44 per s, only 10-fold less than the fastest self-sufficient P450 reported, BM3. Interestingly, the presence of oxygen determines the final products of RDX degradation, demonstrating that the degradation chemistry is flexible, but both pathways result in ring cleavage and release of nitrite. Carbon monoxide inhibition is weak and yet the nitroaromatic explosive 2,4,6-trinitrotoluene (TNT) is a potent inhibitor. To test the efficacy of this system for the remediation of groundwater, transgenic Arabidopsis plants expressing both xplA and xplB were generated. They are able to remove saturating levels of RDX from liquid culture and soil leachate at rates significantly faster than those of untransformed plants and xplA-only transgenic lines, demonstrating the applicability of this system for the phytoremediation of RDX-contaminated sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".