Cytochrome P450s in plant terpenoid biosynthesis: discovery, characterization and metabolic engineering
Bibliographic record
Abstract
As the largest family of natural products, terpenoids play valuable roles in medicine, agriculture, cosmetics and food. However, the traditional methods that rely on direct extraction from the original plants not only produce low yields, but also result in waste of resources, and are not applicable at all to endangered species. Modern heterologous biosynthesis is considered a promising, efficient, and sustainable production method, but it relies on the premise of a complete analysis of the biosynthetic pathway of terpenoids, especially the functionalization processes involving downstream cytochrome P450s. In this review, we systematically introduce the biotech approaches used to discover and characterize plant terpenoid-related P450s in recent years. In addition, we propose corresponding metabolic engineering approaches to increase the effective expression of P450 and improve the yield of terpenoids, and also elaborate on metabolic engineering strategies and examples of heterologous biosynthesis of terpenoids in Saccharomyces cerevisiae and plant hosts. Finally, we provide perspectives for the biotech approaches to be developed for future research on terpenoid-related P450.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.030 |
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".