Machine-learning guided elucidation of contribution of individual steps in the mevalonate pathway and construction of a yeast platform strain for terpenoid production.

The production of terpenoids from engineered microbes contributes markedly to the bioeconomy by providing essential medicines, sustainable materials, and renewable fuels. The mevalonate pathway leading to the synthesis of terpenoid precursors has been extensively targeted for engineering. Nevertheless, the importance of individual pathway enzymes to the overall pathway flux and final terpenoid yield is less known, especially enzymes that are thought to be non-rate-limiting. To investigate the individual contribution of the five non-rate-limiting enzymes in the mevalonate pathway, we created a combinatorial library of 243 Saccharomyces cerevisiae strains, each having an extra copy of the mevalonate pathway integrated into the genome and expressing the non-rate-limiting enzymes from a unique combination of promoters. High-throughput screening combined with machine learning algorithms revealed that the mevalonate kinase, Erg12p, stands out as the critical enzyme that influences product titer. ERG12 is ideally expressed from a medium-strength promoter which is the 'sweet spot' resulting in high product yield. Additionally, a platform strain was created by targeting the mevalonate pathway to both the cytosol and peroxisomes. The dual localization synergistically increased terpenoid production and implied that some mevalonate pathway intermediates, such as mevalonate, isopentyl pyrophosphate (IPP), and dimethylallyl pyrophosphate (DMAPP), are diffusible across peroxisome membranes. The platform strain resulted in 94-fold, 60-fold, and 35-fold improved titer of monoterpene geraniol, sesquiterpene α-humulene, and triterpene squalene, respectively. The terpenoid platform strain will serve as a chassis for producing any terpenoids and terpene derivatives.