Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; KAIST Institute for the BioCentury, KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
Curr Opin Biotechnol. 2022 Feb;73:101-107. doi: 10.1016/j.copbio.2021.07.024. Epub 2021 Aug 3.
Metabolic engineering for developing industrial strains capable of overproducing bioproducts requires good understanding of cellular metabolism, including metabolic reactions and enzymes. However, metabolic pathways and enzymes involved are still unknown for many products of interest, which presents a key challenge in their biological production. This challenge can be partly overcome by constructing novel biosynthetic pathways through enzyme and pathway design approaches. With the increase in bio-big data, data-driven approaches using artificial intelligence (AI) techniques are allowing more advanced protein and pathway design. In this paper, we review recent studies on AI-aided protein engineering and design, focusing on directed evolution that uses AI approaches to efficiently construct mutant libraries. Also, recent works of AI-aided pathway design strategies, including template-based and template-free approaches, are discussed.
代谢工程是一种能够开发出能够大量生产生物制品的工业菌株的方法,需要对细胞代谢有很好的理解,包括代谢反应和酶。然而,对于许多有价值的产品,所涉及的代谢途径和酶仍然未知,这在其生物生产中是一个关键的挑战。通过酶和途径设计方法构建新的生物合成途径,可以部分克服这一挑战。随着生物大数据的增加,使用人工智能 (AI) 技术的数据驱动方法使得更先进的蛋白质和途径设计成为可能。本文综述了人工智能辅助蛋白质工程和设计的最新研究进展,重点介绍了利用人工智能方法高效构建突变文库的定向进化。此外,还讨论了人工智能辅助途径设计策略的最新工作,包括基于模板和无模板的方法。