The functional importance of bacterial oxidative phosphonate pathways
Bibliographic record
Abstract
Organophosphonates (Pns) are a unique class of natural products characterized by a highly stable C-P bond. Pns exhibit a wide array of interesting structures as well as useful bioactivities ranging from antibacterial to herbicidal. More structurally simple Pns are scavenged and catabolized by bacteria as a source of phosphorus. Despite their environmental and industrial importance, the pathways involved in the metabolism of Pns are far from being fully elucidated. Pathways that have been characterized often reveal unusual chemical transformations and new enzyme mechanisms. Among these, oxidative enzymes play an outstanding role during the biosynthesis and degradation of Pns. They are to a high extent responsible for the structural diversity of Pn secondary metabolites and for the break-down of both man-made and biogenic Pns. Here, we review our current understanding of the importance of oxidative enzymes for microbial Pn metabolism, discuss the underlying mechanistic principles, similarities, and differences between pathways. This review illustrates Pn biochemistry to involve a mix of classical redox biochemistry and unique oxidative reactions, including ring formations, rearrangements, and desaturations. Many of these reactions are mediated by specialized iron-dependent oxygenases and oxidases. Such enzymes are the key to both early pathway diversification and late-stage functionalization of complex Pns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".