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使用DeepSEED进行深度侧翼序列工程,以实现高效的促销器设计

Pengcheng Zhang¹, Haochen Wang¹, Hanwen Xu¹

¹Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China.

Nature communications

|October 9, 2023

概括

一个人工智能框架DeepSEED通过整合专家知识和深度学习来设计合成促进器. 它通过揭示隐含的序列特征来改善促进子特性,推进合成生物学应用.

科学领域:

合成生物学合成生物学
计算生物学是一种计算生物学.
基因组学就是基因组学.

背景情况:

促进器设计对于合成生物学至关重要,传统上依赖于已知的转录因子结合位 (TFBS).
经常被忽视的cis-regulatory元素的侧面序列,但影响促进器功能.
深度学习擅长识别大型生物数据集中的复杂模式.

研究的目的:

引入DeepSEED,这是一个人工智能辅助的框架,用于高效的合成促进器设计.
利用深度学习来揭示侧面序列在促进器属性的作用.
改进各种生物系统中现有的促进器设计.

主要方法:

开发了DeepSEED,这是一个结合专家知识和深度学习的AI框架.
应用DeepSEED来设计大肠杆菌和哺乳动物细胞的合成促进剂.
在侧边区域分析隐性序列特征,包括k-mer频率和DNA形状.

主要成果:

DeepSEED成功地改善了构成性,IPTG诱导性和Dox诱导性促进体的特性.
该框架证明了侧边序列在确定促销者特征中的重要性.
鉴定了DeepSEED捕获的隐性序列特征,例如k-mer频率和DNA形状.

结论:

通过整合人工智能和生物专业知识,DeepSEED提供了一种高效的合成促进器设计方法.
这项研究强调了侧翼序列的重要性,这些序列在推广器工程中以前被低估了.
隐性序列特征的AI驱动分析可以显著提高功能合成促进体的设计.

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