ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway

Abstract

Repurposing an organelle for specialized metabolism provides an avenue for fermentable, unicellular organisms such as Saccharomyces cerevisiae to mimic compartmentalization of metabolic pathways within different plant tissues. Peroxisomes are attractive organelles for repurposing as they are not required for yeast viability when grown on glucose and can efficiently compartmentalize heterologous enzymes to enable physical separation of cytosolic native metabolism and peroxisomal engineered metabolism. However, when not required, peroxisomes are repressed, leading to low functional capacities for heterologous proteins. Here we engineer peroxisomes with enhanced functional capacities, with the goal of compartmentalizing up to eight metabolic enzymes to enhance titers. We implement a machine learning pipeline that allows the identification of factors to overexpress, culminating in a 137% increase in peroxisome functional capacity compared to a wild-type strain. Improved pathway compartmentalization enables an 80% increase in the biosynthesis titers of the monoterpene geraniol, up to 9.5 g L^-1.