NPtagM: A Tailoring Enzyme Genome Mining Toolkit and Its Application in Terpenoid P450s from Phytopathogenic Fungi
Bibliographic record
Abstract
Terpenoids derived from phytopathogenic fungi are major participants in interactions among microorganisms, plants, and animals. The modifications catalyzed by cytochrome P450s significantly influence the structural and bioactivity diversity of the terpenoids. To conduct genome mining of P450s in pathogenic fungi, in this study, we developed a new software called N atural P roducts Ta iloring Enzymes G enome M ining (NPtagM). By optimizing the workflow and gene prediction software, NPtagM demonstrated a 3-fold increase in the number of predicted P450s and an 8-fold reduction in runtime compared to antiSMASH. We then used it to extract 1189 dereplicated terpenoid P450s from our in-house fungal genomes. Using a sequence similarity network analysis, we identified a family that potentially produced eremophilane-type sesquiterpenoids. The heterologous expression in Aspergillus oryzae resulted in the production of two new and four known eremophilanes. Our results highlight the potential of NPtagM in genome mining for tailoring enzymes from phytopathogenic fungi.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".