Discovery of a glyphosate oxidase in nature
Bibliographic record
Abstract
Glyphosate is the most used herbicide on Earth. After a half-century of use we know only two biodegradative pathways, each of which appears to degrade glyphosate incidentally. One pathway begins with oxidation of glyphosate catalysed by glycine oxidase (GO). To date, no naturally occurring GO enzymes preferentially oxidize glyphosate but nonetheless are sufficiently active to initiate its degradation. However, GO enzymes that preferentially oxidize glyphosate over glycine-i.e. glyphosate oxidases (GOXs)-may have evolved in environments facing prolonged glyphosate exposure. To test this hypothesis, we screened a metagenomic library from glyphosate-exposed agricultural soil and identified a GOX from clone 11AW19 (GO19) that prefers glyphosate over glycine by four orders of magnitude. This is the first GO isolated from a natural source exhibiting a glyphosate preference. Not only have we discovered the first GOX in nature, but we have also demonstrated the utility of functional metagenomics to find a GOX with greater catalytic efficiency and specificity than those engineered using directed evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".