Strangers in a foreign land: 'Yeastizing' plant enzymes

TL;DR

This paper proposes a machine learning-based computational pipeline to redesign plant enzymes for improved functionality in yeast, aiming to facilitate plant pathway engineering in microbial platforms.

Contribution

It introduces a novel data-driven machine learning framework to generalize enzyme adaptation rules, reducing labor-intensive customization for each enzyme.

Findings

Plant and yeast enzymes have distinct sequence features.

The machine learning model can extract 'yeastizing' rules.

Potential integration into a full design-build-test cycle.

Abstract

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.