深度学习辅助设计新型促进器在大肠杆菌中
Xinglong Wang1,2, Kangjie Xu1,2, Yameng Tan1,2
1Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology Jiangnan University 1800 Lihu Road Wuxi Jiangsu 214122 China.
Advanced genetics (Hoboken, N.J.)
|December 15, 2023
概括
研究人员开发了深度学习 (DL) 工具,以设计新的*Escherichia coli*促销者. 虽然许多生成的促进子是活跃的,但它们的强度有所不同,这凸显了在合成生物学和代谢工程中需要进一步优化.
科学领域:
- 合成生物学 合成生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 深度学习 (DL) 模型擅长识别DNA序列,包括促进区,并预测它们的功能强度.
- 设计具有可预测活性的合成促进剂对于代谢工程和合成生物学中的应用至关重要.
研究的目的:
- 通过使用DL模型的组合,探索活性大肠杆菌促进体的可控制设计.
- 开发DL工具,用于生成新的促销器序列,并准确预测它们的活性和强度.
主要方法:
- 开发"DRSAdesign",一种DL模型,使用扩散方法来生成新的促进器序列.
- 创建了"Ndesign",一个DL模型,用于生成具有特定sigma70促进器动机 (-35和 -10) 的序列,并预测它们的强度.
- 产生的促进者的实验验证,以评估活动和多样性.
主要成果:
- DRSAdesign产生了50个新型促进者,其中45个被发现是活跃和多样化的,尽管大多数表现出较低的活动.
- Ndesign DL模型实现了促进体强度的中等预测准确性 (皮尔森相关系数为0.49和0.43).
- 识别关键的6-mer图案,这些图案对于sigma70促进体识别和强度预测至关重要.
结论:
- 可以有效地使用DL工具来设计和评估合成促进剂.
- 促进子功能与特定DNA动机的排列密切相关.
- 这项工作推进了DL辅助设计的代谢工程应用.
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