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Xia Y, Du X, Liu B, Guo S, Huo YX. Species-specific design of artificial promoters by transfer-learning based generative deep-learning model. Nucleic Acids Res 2024; 52:6145-6157. [PMID: 38783063 PMCID: PMC11194083 DOI: 10.1093/nar/gkae429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.
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Affiliation(s)
- Yan Xia
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Xiaowen Du
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Shuyuan Guo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Yi-Xin Huo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Tangshan Research Institute, Beijing Institute of Technology, Hebei 063611, China
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Xu K, Yu S, Wang K, Tan Y, Zhao X, Liu S, Zhou J, Wang X. AI and Knowledge-Based Method for Rational Design of Escherichia coli Sigma70 Promoters. ACS Synth Biol 2024; 13:402-407. [PMID: 38176073 DOI: 10.1021/acssynbio.3c00578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
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 Escherichia coli. The results suggested that the combined method may be effective for promoter engineering.
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Affiliation(s)
- 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
- Science Center for Future Foods, 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
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Kun 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
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, 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
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Xinyi Zhao
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Song Liu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - 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
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
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