4.7 Article

Enzyme activity engineering based on sequence co-evolution analysis

期刊

METABOLIC ENGINEERING
卷 74, 期 -, 页码 49-60

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymben.2022.09.001

关键词

Sequence evolution; Enzyme engineering; Sequence design; Metabolic engineering; Co -evolution analysis

资金

  1. Korean National Research Foundation [2021R1A2B5B01001903, 2021R1A6A3 A03043982, 2019R1A2C2084631, 2020R1A6A1A03047902]
  2. Ministry of Oceans and Fisheries [20150242]
  3. Artificial Intelligence Graduate School Program, POSTECH [2019-001906]
  4. National Research Foundation of Korea [2020R1A6A1A03047902, 2019R1A2C2084631, 2021R1A2B5B01001903] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

The utility of engineering enzyme activity is expanding as biotechnology develops. Traditional methods have limited applicability, but a strategy called sequence co-evolutionary analysis has been developed to improve enzyme activity and control reaction efficiency. This strategy has successfully predicted enzyme activities and controlled the activities of different enzymes in chemical production.
The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3 '-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, alpha-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substratebinding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.

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