Promiscuous and regiospecific Vinca minor hydroxylases for opioid akuammine biosynthesis and monoterpenoid indole alkaloid diversification
Bibliographic record
Abstract
The medicinal plant Vinca minor produces vincamine, a compound used for neurodegenerative diseases, along with a diverse array of monoterpenoid indole alkaloids (MIAs) primarily within the aspidosperma and akuammiline subclasses. While recent studies have elucidated the core biosynthetic pathways for these subclasses, the transformations of key intermediates into the vast diversity of naturally occurring alkaloids remain poorly understood. In this study, we identify and characterize two promiscuous cytochrome P450 monooxygenases (CYPs) in V. minor : vincaminoreine/pericyclivine 10-hydroxylase (VmV10H) and pseudoakuammigine 10-hydroxylase (VmPs10H), both exhibiting high substrate versatility. VmV10H catalyzes the hydroxylation of structurally diverse MIAs, including vincaminoreine, pericyclivine, apparicine, and akuammidine, while VmPs10H demonstrates a preference for akuammiline type MIAs such as pseudoakuammigine, picrinine, and strictamine. Homology modeling and substrate docking reveal active site architecture of these enzymes, suggesting a consistent mechanism for C10 hydroxylation across all substrates. The discovery of VmV10H and VmPs10H not only broadens our understanding of MIA biosynthesis but also expands the enzymatic toolkit for the metabolic engineering of pharmaceutical MIAs, including akuammine, a μ-opioid receptor agonist with analgesic properties. • Metabolic profiling reveals tissue-specific alkaloid accumulation in Vinca minor. • A new V. minor enzyme catalyzes vincaminoreine and pericyclivine 10-hydroxylation. • A second enzyme hydroxylates pseudoakuammigine/picrinine/strictamine at C10. • Homology modeling shows key features for reaction promiscuity and regiospecificity. • Pseudoakuammigine 10-hydroxylase enables the biosynthesis of the opioid akuammine.
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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".