Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.
Department of Systems and Synthetic Metabolism, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
Microb Biotechnol. 2024 Sep;17(9):e14525. doi: 10.1111/1751-7915.14525.
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 the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-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.
在微生物平台中表达植物代谢途径是生产许多所需植物化合物的一种高效、经济有效的解决方案。由于酵母是真核生物，因此常被选为首选平台。然而，由于植物酶在酵母中表达常常导致失败，因为这些酶对异源酵母细胞环境适应性差。在这里，我们首先总结了目前用于优化酵母中植物酶性能的工程方法。这些方法的一个关键局限性是它们劳动强度大，必须针对每个单独的酶进行定制，这极大地阻碍了植物途径在细胞工厂中的建立。针对这一挑战，我们提出开发一种具有成本效益的计算管道，重新设计植物酶以更好地适应酵母细胞环境。这一主张的基础是令人信服的证据，即植物和酵母酶表现出独特的序列特征，这些特征在酶家族中具有普遍性。因此，我们引入了一个数据驱动的机器学习框架，旨在从天然蛋白质序列变化中提取“酵母化”规则，这些规则可以广泛应用于所有酶。此外，我们还讨论了将机器学习模型集成到完整的设计-构建-测试周期的潜力。
