Biosynthesis of novel cannabigerolic acid derivatives by engineering the substrate specificity of aromatic prenyltransferase
Bibliographic record
Abstract
Introduction Cannabinoids possess significant therapeutic potential, but their natural chemical diversity derived from plant biosynthesis is limited. Efficient biotransformation processes are required to expand the range of accessible cannabinoids. This study aimed to enhance the selective biosynthesis of cannabigerolic acid (CBGA) and its derivatives with varying aliphatic chain lengths, which serve as key precursors to various cannabinoids. Methods We employed computational modeling and structure-guided mutagenesis to engineer the aromatic prenyltransferase NphB. Mutants were designed via in silico docking analyses to optimize substrate orientation and catalytic distance. The variants were expressed in E. coli , and their catalytic efficiencies were evaluated through in vivo whole-cell and in vitro enzymatic assays. Products were identified and quantified by UHPLC-MS. Results Engineered NphB variants exhibited significant improvements, with triple mutants achieving a 7-fold increase in CBGA production and a 4-fold increase in cannabigerovarinic acid production. Additionally, a single mutant also enhanced the synthesis of 3-geranyl orsellinic acid by 1.3-fold. Notably, novel enzymatic activity was identified that enabled the biosynthesis of 3-geranyl-2,4-dihydroxybenzoic acid. Structural analyses revealed that the mutations improved the spatial positioning of aromatic substrates relative to the co-substrate geranyl pyrophosphate. Discussion This study demonstrates the feasibility of enzyme design to tailor prenyltransferase specificity for the production of diverse CBGA derivatives. These findings lay the groundwork for the microbial production of novel cannabinoids and offer promising potential for the development of scalable biocatalytic systems for therapeutic and industrial applications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".