| Literature DB >> 34358728 |
Woo Dae Jang1, Gi Bae Kim2, Yeji Kim2, Sang Yup Lee3.
Abstract
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.Entities:
Mesh:
Year: 2021 PMID: 34358728 DOI: 10.1016/j.copbio.2021.07.024
Source DB: PubMed Journal: Curr Opin Biotechnol ISSN: 0958-1669 Impact factor: 9.740