Structure-Guided Engineering of Prenyltransferase NphB for High-Yield and Regioselective Cannabinoid Production

Synthetic biology efforts for cannabinoid research have seen a rapid expansion in recent years. This is in response to the increasing awareness and legalization of the secondarymetabolites from Cannabis sativa, dubbed the green rush. Intransgenic synthetic biology applications, NphB is a promiscuousprenyltransferase fromStreptomycessp. often used as a replacementin the prenylation step producing the cannabinoid cannabigerolicacid (CBGA), the key precursor to many other cannabinoids.However, its application as a CBGA synthase replacement islimited by its nonspecific regioselectivity in producing a sideproduct along with CBGA. Herein, we demonstrated a detailedand extensive computational structure-guided approach inidentifying target residues of mutation for engineering NphB foroptimal CBGA production. Our comprehensive computational workflow has led to the discovery of several highly regiospecificvariants that produce CBGA exclusively, with the best-performing V49W/Y288P variant having a 13.6-fold yield improvement,outperforming all previous work on NphB enzyme engineering. We subsequently investigated the effects of these mutations by X-raycrystallographic studies of the mutant variants and performed molecular dynamics simulations to uncover an interplay of a H-bonding network and an optimal ligand orientation that favors the CBGA production over the side product. Collectively, this studynot only recapitulates the utility of computational tools in informing and accelerating experimental design but also contributes to abetter understanding of molecular mechanisms that govern enzyme regioselectivity and readily aids in cannabinoid synthetic biology production for future research into maximizing their therapeutic potential.

Automated summary

In recent years, synthetic biology has rapidly developed in cannabinoid research, particularly in engineering variants of the NphB enzyme for optimal CBGA production. This study utilized a computational structure-guided approach to identify highly regiospecific variants that exclusively produce CBGA, leading to a better understanding of the molecular mechanisms governing enzyme regioselectivity. These findings contribute to the advancement of cannabinoid synthetic biology production and maximizing their therapeutic potential.