  Expressing plant metabolic pathways in microbial platforms is an efficient,
cost-effective solution for producing many desired plant compounds. As
eukaryotic organisms, yeasts are often the preferred platform. However,
expression of plant enzymes in a yeast frequently leads to failure because the
enzymes are poorly adapted to the foreign yeast cellular environment. Here we
first summarize current engineering approaches for optimizing performance of
plant enzymes in yeast. A critical limitation of these approaches is that they
are labor-intensive and must be customized for each individual enzyme, which
significantly hinders the establishment of plant pathways in cellular
factories. In response to this challenge, we propose the development of a
cost-effective computational pipeline to redesign plant enzymes for better
adaptation to the yeast cellular milieu. This proposition is underpinned by
compelling evidence that plant and yeast enzymes exhibit distinct sequence
features that are generalizable across enzyme families. Consequently, we
introduce a data-driven machine learning framework designed to extract
'yeastizing' rules from natural protein sequence variations, which can be
broadly applied to all enzymes. Additionally, we discuss the potential to
integrate the machine learning model into a full design-build-test-cycle.
