Prenylated phenols (PPs) are ubiquitous in organisms and exhibit a wide range of activities of medical importance, including as antidepressants, anticancer agents, anti-inflammatories and phytoestrogens.
The commercial use of PPs as drugs or drug candidates is often hindered by low yields when isolated from plant material or due to complex chemical syntheses.
Biotechnological production offers a sustainable alternative, but requires the identification of biosynthetic genes.
The bottleneck step in the formation of PPs is catalyzed by aromatic prenyltransferases (aPTs).
However, our knowledge about the identification, characterization, specificity and comparative structural studies of aPTs is still very incomplete.
To fill these knowledge gaps, PhenoPren will combine artificial intelligence (AI)-assisted enzyme mining with transcriptomic analysis and experimental biochemical research to discover new enzymes.
In addition, computational optimization of aPTs will provide tailored enzymes for the production of specific PPs.
Optimized and characterized engineered enzymes will subsequently be used in microbial cell factories for the production of specific PPs for medical purposes.
Novel PPs derivatives are formed by the conversion of non-natural (poly)phenolics from waste products.