使用蛋白质语言模型和多个实例学习来预测病毒与宿主之间的关联
Dan Liu1, Francesca Young1,2, Kieran D Lamb1
1MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom.
PLoS computational biology
|November 19, 2024
概括
我们开发了EvoMIL,这是一种深度学习工具,只使用病毒序列来预测病毒宿主. 这种方法可以识别关键的病毒蛋白,提高我们对病毒宿主特异性的理解.
科学领域:
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 预测病毒与宿主之间的关联对于识别病毒宿主至关重要,尤其是在大多数病毒宿主仍然未知的微生物组中.
- 了解这些关联有助于发现可能感染人类和动物的新型病毒.
研究的目的:
- 介绍EvoMIL,这是一种新的深度学习方法,用于仅使用病毒序列来预测病毒与宿主之间的关联.
- 识别参与宿主特异性决定的关键病毒蛋白.
主要方法:
- EvoMIL集成了预训练的蛋白质语言模型 (ESM) 与基于注意力的多实例学习,用于蛋白质水平的预测.
- 该方法利用蛋白质嵌入,优于传统的序列组成特征,如氨基酸和DNA k-mers.
主要成果:
- 埃沃米尔显示出显著的F1得分中位数改善:原生宿主10.8%,16.2%,4.9%,真核宿主1.7%,6.6%,11.5%.
- 二元分类器实现了高AUC值 (>0.95对于 prokaryotic,0.8-0.9对于真核宿主).
- 确定了对宿主特异性有贡献的关键病毒蛋白.
结论:
- 蛋白质嵌入提供了强大的信号,用于预测病毒与宿主之间的关联.
- 埃沃米尔提供了一种有效的方法来识别病毒宿主和关键蛋白质,从而提高我们对病毒与宿主相互作用的理解.
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