人工智能引导的设计和预测模型合成大肠杆菌促进者通过综合 -10/-35盒工程
Xuan Zhou1, Nana Ding1, Shenghu Zhou1
1School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China.
ACS synthetic biology
|January 27, 2026
概括
研究人员创建了一个合成促进器库,以了解DNA序列如何影响基因活性. 他们开发了一个人工智能平台,用于精确的促销器设计,从而实现对大肠杆菌的基因表达的定制控制.
科学领域:
- 分子生物学分子生物学
- 合成生物学 合成生物学
- 生物信息学是一种生物信息学.
背景情况:
- 促进者调节基因转录, -10和 -35盒对于强度至关重要.
- 促进器序列和转录输出之间的定量关系是不太了解,阻碍合理的促进器设计.
研究的目的:
- 开发一个量化框架,将促进体序列组成与转录活性联系起来.
- 使用人工智能设计具有可预测和可调节的表达水平的合成促进剂.
主要方法:
- 通过在 -10 和 -35 盒中改变RNA聚合酶结合能来构建合成促进子库.
- 光激活细胞分类和测序以识别功能促进体.
- 开发一个集结卷积神经网络和生成对抗网络的深度学习平台,用于促进器预测和设计.
主要成果:
- 鉴定了20799个不同的促进体,其表达强度范围为80倍.
- 发现了特定的 -35 盒序列,在各种 -10 合作伙伴之间产生了高活性.
- 人工智能平台在预测促销者强度 (r=0.84) 和设计具有用户定义强度 (r=0.85) 的促销者方面取得了高准确性.
结论:
- 建立了一个双向深度学习框架,将促进器序列 (-10/-35框) 与转录活动连接起来.
- 在大肠杆菌中扩大功能性促进体的序列多样性.
- 为理性促销器工程提供了一个预测平台,并破译了组合型图案相互作用.
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