基于深度学习的蛋白质-蛋白质相互作用分析,用于预测SARS-CoV-2的感染性和变体演变
Guangyu Wang1, Xiaohong Liu2,3, Kai Wang4
1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China. guangyu.wang24@gmail.com.
Nature medicine
|July 31, 2023
概括
一个AI框架UniBind可以预测病毒变异如何影响蛋白质结合. 该工具有助于预测新的SARS-CoV-2变种,并了解早期预警系统的病毒演变.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 人工智能的人工智能
背景情况:
- 蛋白质与蛋白质的相互作用对于宿主-病原体动态和病毒进化至关重要.
- 了解病毒变异如何改变这些相互作用是预测病原菌株的出现的关键,比如新的SARS-CoV-2变异.
研究的目的:
- 开发和验证基于AI的框架UniBind,用于预测蛋白质变体对结合亲和关系的影响.
- 评估UniBind在预测SARS-CoV-2变种对宿主和抗体相互作用的影响方面的能力.
主要方法:
- UniBind以残留和原子水平的图形表示蛋白质,集成3D结构和结合亲和数据.
- 该框架采用多任务学习来实现异构的生物数据集成.
- 对基准数据集和实验验证进行了系统测试.
主要成果:
- UniBind准确且可扩展地预测了SARS-CoV-2尖端蛋白变体对ACE2受体和中和抗体的结合 afinities 的影响.
- 跨物种分析表明,UniBind在预测宿主易感性和病毒进化趋势方面具有实用性.
结论:
- UniBind提供了一种强大的in silico方法,用于预测病毒变异对蛋白质-蛋白质相互作用的影响.
- 该框架可以作为新出现的SARS-CoV-2变种的早期预警系统,并推进一般蛋白质-蛋白质相互作用研究.
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