AI and Knowledge-Based Method for Rational Design of Sigma70 Promoters.

Journal: ACS synthetic biology
PMID: 38176073

Abstract

Expanding sigma70 promoter libraries can support the engineering of metabolic pathways and enhance recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 promoters. Strong sigma70 promoters were identified by using high-throughput screening (HTS) with enhanced green fluorescent protein (eGFP) as a reporter gene. The features of these strong promoters were adopted to guide promoter design based on our previous reported deep learning model. In the following case study, the obtained strong promoters were used to express collagen and microbial transglutaminase (mTG), resulting in increased expression levels by 81.4% and 33.4%, respectively. Moreover, these constitutive promoters achieved soluble expression of mTG-activating protease and contributed to active mTG expression in . The results suggested that the combined method may be effective for promoter engineering.

Authors

  • Kangjie Xu
    Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
  • Shangyang Yu
    Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Yameng Tan
    Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
  • Xinyi Zhao
  • Song Liu
    Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
  • Jingwen Zhou
    Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China; Jiangsu Province Basic Research Center for Synthetic Biology, Jiangnan University, Wuxi 214122, China. Electronic address: zhoujw1982@jiangnan.edu.cn.
  • Xinglong Wang
    Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.

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