从序列架构到强度控制的大肠杆菌核心促进体的深度学习引导可编程设计
Xuan Zhou1, Renxu Feng2,3, Nana Ding1
1School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China.
Nucleic acids research
|September 3, 2025
概括
我们开发了一个封闭循环平台, 我们的人工智能模型准确地预测并生成具有所需强度的促进序列.
科学领域:
- 合成生物学
- 计算生物学
- 分子生物学
背景情况:
- 核心促进器调节转录启动,但难以预测和设计.
- 现有的人工智能方法对于推动者工程具有有限的概括性.
研究的目的:
- 开发一个闭环平台,用于端到端的核心发起人工程.
- 能够准确地预测和产生具有定义强度的细菌核心促进剂序列.
主要方法:
- 集成的理性图书馆设计,预测模型 (转换器) 和生成优化 (扩散模型).
- 使用突变-条码-反向测序来构建一个大型合成促进子库 (112,955个变体).
- 建立了一个模块化,可扩展和可扩展的促销商设计平台.
主要成果:
- 变压器模型实现了0.87的皮尔森相关性来预测促进器的强度.
- 综合系统显示设计与测量的相关性为0.95和高精度 (R=0.93).
- 设计的促进器保持了预期的强度梯度,显示了强大的功能.
结论:
- 开发的平台可以通过深度学习引导Escherichia coli核心促进者的可编程设计.
- 这可以通过工程发起物序列来实现精确的转录控制.
- 该平台为合成生物学应用提供了可扩展的解决方案.
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