活动云组织Shine-Dalgarno序列以指导Escherichia coli中的翻译工程
Pavel Zach1, Yadira Boada1, Jesús Pico1
1Synthetic Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, València 46022, Spain.
ACS omega
|February 23, 2026
概括
这项研究引入了一个数据驱动的框架,通过将Shine-Dalgarno (SD) 核心变体组织成可预测的活动云来预测地调整细菌翻译. 这种方法简化了对大肠杆菌的基因表达控制,并为研究人员提供了更可靠的方法.
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:
- 调整细菌翻译对于基因表达控制至关重要.
- 通过Shine-Dalgarno (SD) 动机调整翻译率的现有方法缺乏可预测性.
- 领导序列,间隔区域和编码环境的变化加剧了不可预测性.
研究的目的:
- 开发一个可预测的框架,用于细菌翻译的粗粒度调整.
- 将Shine-Dalgarno (SD) 核心变体组织成可解释的活动云,以保持一致的表达水平.
- 根据目标有效翻译率 (ETR) 选择SD核心,提供数据驱动的双向工作流.
主要方法:
- 开发了一个粗粒度的框架,专注于埃舍里奇亚大肠杆菌的核糖体结合部位 (RBS) 中的SD核心图案.
- 组织SD核心变体成"活动云",代表一致的表达级别.
- 通过使用独立的高吞吐量数据集和开放的Web界面,验证了框架的稳定性和预测效用.
主要成果:
- 该框架成功地将SD核心变体组织成活动云,使基因表达的可预测粗粒度控制成为可能.
- 数据驱动的方法优先考虑SD核心选择,以减轻侧面序列的变化,简化下游微调.
- 该研究表明,基于所需的ETRs选择候选SD核心的双向工作流.
结论:
- 拟议的框架提供了一种可预测,可解释和数据驱动的方法,用于大肠杆菌中翻译启动的粗粒度调整.
- 这种方法简化和提高基因表达调整的可预测性,促进研究和合成生物学应用.
- 该框架为SD核心引导的翻译调整提供了一个可操作的地图,并且可以扩展到其他细菌物种.
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