使用AlphaFold 3辅助拓深度学习对快速病毒进化的快速响应.
JunJie Wee1, Guo-Wei Wei1,2,3
1Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
ArXiv
|November 28, 2024
概括
使用AlphaFold 3和拓深度学习的新计算策略准确地预测病毒突变和结合能量的变化,帮助快速应对像SARS-CoV-2这样的不断发展的传染性病毒.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 结构生物学是结构生物学.
背景情况:
- 包括SARS-CoV-2在内的病毒快速演变,需要更快的追踪方法,诊断和开发疫苗和单克隆抗体 (mAbs) 等对策.
- 目前的计算工具,如拓深度学习 (TDL),需要广泛的实验数据,如深度突变扫描 (DMS) 和3D蛋白质-蛋白质相互作用 (PPI) 复杂结构,这些复杂结构需要大量的时间和成本来获得.
研究的目的:
- 为了开发一种高效的计算方法,AlphaFold 3 (AF3) 辅助的多任务拓拉普拉西安 (MT-TopLap) 策略,以预测病毒突变对蛋白质-蛋白质相互作用 (PPI) 和结合自由能量 (BFE) 的影响.
- 通过拓数据分析 (TDA) 和深度学习,提高深度突变扫描 (DMS) 和BFE变化的预测能力,减少对实验结构的依赖.
主要方法:
- 拟议的MT-TopLap战略将AlphaFold 3 (AF3) 集成为结构预测与拓数据分析 (TDA) 模型,特别是持久的拉普拉西亚 (PL).
- 这种方法从蛋白质-蛋白质相互作用 (PPI) 复杂结构中提取拓和几何特征,以预测病毒突变时的结合自由能量 (BFE) 和深度突变扫描 (DMS) 的变化.
- 该方法使用四个SARS-CoV-2尖端受体结合域 (RBD) 和人类血管酶转化酶-2 (ACE2) 复合物的实验DMS数据集进行了验证.
主要成果:
- AF3辅助的MT-TopLap策略表现出强的性能,与使用实验结构相比,降解最小 (皮尔森相关系数 (PCC) 平均下降1.1%,根平均平方误差 (RMSE) 增加9.3%).
- 该模型在SARS-CoV-2 HK.3变种DMS数据集上测试时达到0.81的PCC,表明BFE变化的准确预测.
- 该策略被证明可以适应新的实验数据,证实其实时应用的潜力.
结论:
- 辅助AF3的MT-TopLap策略提供了一个高效和准确的计算工具,用于预测病毒突变对PPI和BFE的影响.
- 这种方法可以通过改善病毒追踪,诊断以及治疗和疫苗的设计来加速对新出现的传染性病毒的反应.
- 该方法适应新数据的能力凸显了其在解决快速演变的病原体带来的挑战方面的价值.
关键词:
阿尔法 折叠3 3在SARS-CoV-2变种中.拓学深度学习 (deep learning) 是一种学习方式.深度突变扫描 (deep mutational scanning) 是一种对突变进行深度扫描的方法.蛋白质与蛋白质的相互作用更多相关视频
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