Applications of artificial intelligence to enzyme and pathway design for metabolic engineering

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dc.contributor.authorJang, Woo Daeko
dc.contributor.authorKim, Gi Baeko
dc.contributor.authorKim, Yejiko
dc.contributor.authorLee, Sang Yupko
dc.date.accessioned2021-08-09T05:50:08Z-
dc.date.available2021-08-09T05:50:08Z-
dc.date.created2021-08-09-
dc.date.issued2022-02-
dc.identifier.citationCURRENT OPINION IN BIOTECHNOLOGY, v.73, pp.101 - 107-
dc.identifier.issn0958-1669-
dc.identifier.urihttp://hdl.handle.net/10203/287070-
dc.description.abstractMetabolic 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.-
dc.languageEnglish-
dc.publisherCURRENT BIOLOGY LTD-
dc.titleApplications of artificial intelligence to enzyme and pathway design for metabolic engineering-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume73-
dc.citation.beginningpage101-
dc.citation.endingpage107-
dc.citation.publicationnameCURRENT OPINION IN BIOTECHNOLOGY-
dc.identifier.doi10.1016/j.copbio.2021.07.024-
dc.contributor.localauthorLee, Sang Yup-
dc.description.isOpenAccessN-
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CBE-Journal Papers(저널논문)
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