开发mRNA疫苗的新型深度生成模型:设计5' UTRs与1 - 甲基 - 伪乌里丁修饰
Xiaoshan Tang1, Miaozhe Huo2, Yuting Chen1
1Institute of Systems Genetics, Department of Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610000, China.
Acta pharmaceutica Sinica. B
|April 4, 2024
概括
设计最佳的5'非翻译区域 (UTR) 对于N1-甲基-伪氨基酸修饰mRNA疫苗至关重要. 一个新的机器学习工具,Smart5UTR,识别出优异的5' UTR,提高了针对SARS-CoV-2变种的疫苗疗效.
科学领域:
- 分子生物学分子生物学
- 疫苗学 疫苗学 疫苗学
- 生物信息学是一种生物信息学.
背景情况:
- 通过5'未翻译区域 (UTR) 的有效翻译是mRNA疫苗疗效的关键.
- 在mRNA中的N1-甲基伪尤里丁 (m1Ψ) 修改影响5' UTR转化效率.
- 对于m1Ψ修饰的mRNA,最佳的5' UTR与未修饰的mRNA不同.
研究的目的:
- 开发一种专门的工具,用于为m1Ψ修饰的mRNA设计最佳的5' UTR.
- 为解决使用高表达内源基因5'UTRs用于m1Ψ修饰mRNA疫苗的局限性.
- 通过优质的5' UTR设计,改进mRNA疫苗的研发.
主要方法:
- 开发一种基于机器学习的新型工具,Smart5UTR.
- 使用深度生成模型进行*in silico*识别高级m1Ψ-5' UTRs.
- 在Smart5UTR.中使用量身定制的损失函数和网络架构.
主要成果:
- 智能5UTR成功为m1Ψ修饰的mRNA设计了优质的5' UTR.
- 智能5UTR克服了现有设计模型的局限性.
- 智能5UTR设计的5' UTRs显著增强了针对Delta和Omicron变种的COVID-19mRNA疫苗的抗体标位.
结论:
- 智能5UTR是设计有效的m1Ψ-5' UTRs用于mRNA疫苗的宝贵工具.
- 优化的5' UTR显著提高mRNA疫苗的性能.
- 这种方法对推进mRNA疫苗技术具有很大的前景.
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