使用基于人工智能的进化和抗原性模型进行流感疫苗菌株选择
Wenxian Shi1, Jeremy Wohlwend2, Menghua Wu2
1Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. wxsh@mit.edu.
Nature medicine
|August 28, 2025
概括
VaxSeer是一种新的计算方法,可以预测疫苗与病毒的匹配,从而提高流感疫苗的有效性. 这种方法通过预测未来的病毒主导性和抗原特征来改善目前的方法.
科学领域:
- 病毒学
- 免疫学
- 计算生物学
- 疫苗学
背景情况:
- 目前的疫苗对流感等快速变异的病毒提供有限的保护.
- 在2012年至2021年期间,美国流感疫苗的有效率平均低于40%.
- 评估疫苗的临床结果通常是追溯的过程.
研究的目的:
- 为预测疫苗候选抗原匹配与未来流通的病毒提出一个in silico方法,VaxSeer.
- 与传统方法相比,评估VaxSeer在选择具有优异抗原匹配的菌株方面的表现.
主要方法:
- 使用测序和抗原性数据开发了VaxSeer.
- 在10年的回顾数据中评估VaxSeer.
- 评估了VaxSeer预测的抗原匹配与流感疫苗实际有效性的相关性.
主要成果:
- VaxSeer 始终发现与循环病毒的经验抗原匹配率高于年度建议.
- 通过VaxSeer预测的抗原匹配与流感疫苗的有效性有很强的相关性.
- 瓦克塞尔的预测也与疾病负担的减少有关.
结论:
- VaxSeer提供了一种有前途的计算方法来增强疫苗菌株的选择.
- 这种框架有可能提高未来的疫苗疗效并减少疾病的影响.
- 抗原匹配的预测可以主动指导疫苗的开发.
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