演变的健身和免疫逃脱:使用蛋白质语言模型对SARS-CoV-2尖端蛋白 (2020-2024) 的回顾性分析
Sihua Peng1,2, Leke Lyu1,3, Ludy Registre Carmola2
1Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, United States.
Frontiers in immunology
|July 3, 2025
概括
SARS-CoV-2 的演变显示出越来越多的适应性和免疫逃脱,由像 JN.1.1 这样的变体驱动. 像CovFit这样的蛋白质语言模型对于跟踪病毒适应和告知公共卫生战略至关重要.
科学领域:
- 病毒学 病毒学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 随着COVID-19的流行,人们越来越需要了解SARS-CoV-2的演变.
- 评估病毒适应性和免疫逃生对于有效的公共卫生干预和疫苗开发至关重要.
研究的目的:
- 分析SARS-CoV-2的进化动态,重点关注影响病毒适应性和免疫逃避的遗传变异.
- 评估CovFit模型在预测病毒健康和免疫逃脱方面的表现.
主要方法:
- 利用蛋白质语言模型,特别是CoVFit模型,来预测健康和免疫逃生指数 (IEI).
- 从2020年到2024年5月,分析了超过250万个SARS-CoV-2尖端序列,包括160,892个变体.
- 对比真实突变者的健身和IEI与中性进化的零模型.
主要成果:
- 从2020年到2024年,在北美样本中观察到平均健身 (0.227到0.930) 和IEI (0.171到0.555) 的显著增加.
- 在全球范围内,真实和随机突变物之间在适应性和IEI中显示出统计学上显著的差异 (p < 0.001),表明强大的选择性压力.
- 确定了JN.1血统是主导的 (到2024年4月为94%的序列),突出了它的进化优势.
结论:
- CoVFit模型为SARS-CoV-2演变提供了宝贵的见解,有助于疫苗设计和公共卫生准备.
- 持续的病毒适应需要持续的监测和适应性策略,利用像CovFit.com这样的工具.
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