预测哺乳动物细胞中信使RNA的翻译效率
Dinghai Zheng1, Logan Persyn2, Jun Wang1
1mRNA Center of Excellence, Sanofi, Waltham, MA, USA.
Nature biotechnology
|July 27, 2025
概括
科学家使用深度学习在人类和小鼠细胞中绘制了翻译效率 (TE). RiboNN从mRNA序列中预测TE,揭示了序列特征如何控制蛋白质生产和基因调节.
科学领域:
- 分子生物学分子生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 在哺乳动物中基于mRNA序列的翻译控制机制尚未完全理解.
- 翻译效率 (TE) 是基因表达的一个关键决定因素.
- 核糖体分析提供了全基因组的翻译测量.
研究的目的:
- 为了创建一个全面的地图集的TE跨不同的哺乳动物细胞类型.
- 根据mRNA序列特征开发TE的预测模型.
- 阐明管理翻译控制的序列编码原则.
主要方法:
- 从3,819个人类和小鼠核糖体分析数据集中生成了TE的全转录组图谱.
- 开发了RiboNN,一个深层卷积神经网络,以及机器学习模型来预测TE.
- 集成序列编码的mRNA特征,包括二核酸,三核酸和位定位.
主要成果:
- RiboNN准确地预测了数百种细胞类型的TE.
- 确定了序列特征的空间定位如何影响TE,反映了核糖体过程性和tRNA丰度.
- 证明了RiboNN能够预测治疗性RNA行为的能力,并解释进化压力.
结论:
- 对于控制mRNA存在一个共同的监管语言.
- 在哺乳动物细胞中,mRNA的翻译,稳定性和局部化是相互关联的过程.
- 序列编码特征为理解和预测翻译控制提供了一个强大的框架.
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