Machine learning-guided olivetolic acid cyclase engineering enables tailored cannabinoid biosynthesis in yeast

Abstract

Cannabinoids comprise a diverse class of bioactive natural products with important therapeutic potential, but efficient microbial production remains limited by pathway bottlenecks and challenges in engineering key biosynthetic enzymes. Here, we develop a machine learning-guided approach to engineer olivetolic acid cyclase (OAC), a critical control point in cannabinoid biosynthesis that governs both pathway flux and product selectivity. We first generated sequence-function data from 152 CsOAC variants spanning homolog screening, recombination, and mutagenesis libraries. Using these measurements, we trained multi-task models to predict pathway-level production of olivetolic acid (OA), divarinic acid (DVA), and competing byproducts, together with a variational autoencoder that captured evolutionary constraints across the broader enzyme family. Across three rounds of iterative design and testing, this approach identified CsOAC variants that substantially increased production and selectivity of both OA and DVA. When introduced into engineered Yarrowia lipolytica strains, these variants enabled production of tetrahydrocannabinolic acid (THCA) and the minor cannabinoid tetrahydrocannabivarinic acid (THCVA) at titers exceeding previous yeast systems. Analysis of top-performing variants revealed mutations influencing substrate selectivity and catalytic performance, providing insight into the determinants of CsOAC function. More broadly, this work demonstrates how machine learning-guided enzyme engineering can improve pathway performance and expand access to major and minor cannabinoids through microbial biosynthesis.