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语言模型学会代表人类流感A(H3) 病毒的抗原性质

Francesco Durazzi¹, Marion P G Koopmans², Ron A M Fouchier²

¹Department of Physics and Astronomy, University of Bologna, 40127, Bologna, Italy.

Scientific reports

|July 1, 2025

概括

使用基因组数据可以预测流感A(H3N2) 病毒的免疫逃逸. 机器学习模型,特别是深度学习,可以识别抗原变异,帮助未来的疫苗更新.

科学领域:

病毒学病毒学
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

流感疫苗的有效性取决于疫苗和循环病毒之间的抗原匹配.
持续评估新出现的流感变种的抗原性质至关重要.
实时基因组监测提出了从基因组数据中预测抗原性质的问题.

研究的目的:

为了比较机器学习方法来预测流感A ((H3N2) HA1蛋白序列的抗原性质.
为了排名对主要抗原变化负责的替代物.
为潜在的疫苗更新确定具有新型抗原性质的变体.

主要方法:

利用流感病毒基因组和湿实验室实验结果的验证链接数据集.
对比深度学习 (BiLSTM,ProtBERT) 和经典机器学习方法.
根据蛋白质序列重建了抗原地图坐标和排序替代.

主要成果:

深度学习模型 (BiLSTM,ProtBERT) 在细粒度抗原变化预测的经典方法中表现优越.
模型有效地确定了影响抗原性质的单氨基酸替代.
基于蛋白质嵌入的最佳性能模型是无病原体的.

结论:

在 silico 模型中,特别是深度学习,可以可靠地预测免疫系统从流感病毒基因组信息中逃脱.
这种方法有助于识别令人担忧的变异,并可能指导未来的疫苗菌株选择.
该方法对预测其他病原体的抗原性质具有潜在的适用性.

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