De novo design of insulated cis-regulatory elements based on deep learning-predicted fitness landscape.

IF 16.6 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Haochen Wang, Yanhui Xiang, Ziming Liu, Wen Yin, Boyan Li, Long Qian, Xiaowo Wang, Chunbo Lou
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引用次数: 0

Abstract

Precise control of gene activity within a host cell is crucial in bioengineering applications. Despite significant advancements in cis-regulatory sequence activity prediction and reverse engineering, the context-dependent effects of host cellular environment have long been neglected, leading to ongoing challenges in accurately modeling regulatory processes. Here, we introduce an insulated design strategy to purify and model host-independent transcriptional activity. By integrating heterologous paired cis- and trans-regulatory modules into an orthogonal host cell, we established a controllable transcriptional regulatory system. Using a deep learning-based algorithm combined with an experimental data purification process, we achieved the de novo design full-length transcriptional promoter sequences driven by a host-independent activity landscape. Notably, this landscape accurately captured the transcriptional activity of the insulated system, enabling the generation of cis-regulatory sequences with desirable sequence and functional diversity for two distinct trans-RNA polymerases. Importantly, their activities are precisely predictable in both bacterial (Escherichia coli) and mammalian (Chinese hamster ovary) cell lines. We anticipated that de novo design strategy can be expanded to other complex cis-regulatory elements by integrating the deep learning-based algorithm with the construction of paired cis- and trans-regulatory modules in orthogonal host systems.

基于深度学习预测适应度景观的绝缘顺式调节元件从头设计。
在生物工程应用中,精确控制宿主细胞内的基因活性是至关重要的。尽管在顺式调控序列活性预测和逆向工程方面取得了重大进展,但宿主细胞环境的上下文依赖效应长期以来被忽视,导致在准确建模调控过程方面持续存在挑战。在这里,我们引入了一种绝缘设计策略来纯化和模拟与宿主无关的转录活性。通过将异源配对的顺式和反式调控模块整合到一个正交的宿主细胞中,我们建立了一个可控的转录调控系统。利用基于深度学习的算法结合实验数据纯化过程,我们实现了由宿主独立活动景观驱动的从头设计全长转录启动子序列。值得注意的是,这幅图准确地捕获了隔离系统的转录活性,使两种不同的反式rna聚合酶能够产生具有理想序列和功能多样性的顺式调控序列。重要的是,它们的活性在细菌(大肠杆菌)和哺乳动物(中国仓鼠卵巢)细胞系中都是可以精确预测的。我们预计,通过将基于深度学习的算法与正交宿主系统中配对顺式和反式调控模块的构建相结合,从头设计策略可以扩展到其他复杂的顺式调控元素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nucleic Acids Research
Nucleic Acids Research 生物-生化与分子生物学
CiteScore
27.10
自引率
4.70%
发文量
1057
审稿时长
2 months
期刊介绍: Nucleic Acids Research (NAR) is a scientific journal that publishes research on various aspects of nucleic acids and proteins involved in nucleic acid metabolism and interactions. It covers areas such as chemistry and synthetic biology, computational biology, gene regulation, chromatin and epigenetics, genome integrity, repair and replication, genomics, molecular biology, nucleic acid enzymes, RNA, and structural biology. The journal also includes a Survey and Summary section for brief reviews. Additionally, each year, the first issue is dedicated to biological databases, and an issue in July focuses on web-based software resources for the biological community. Nucleic Acids Research is indexed by several services including Abstracts on Hygiene and Communicable Diseases, Animal Breeding Abstracts, Agricultural Engineering Abstracts, Agbiotech News and Information, BIOSIS Previews, CAB Abstracts, and EMBASE.
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