一种深度学习方法,预测COVID-19治疗药物和疫苗对新兴变种的活性
Robert P Matson1, Isin Y Comba1,2, Eli Silvert1
1nference, Cambridge, MA, 02139, USA.
NPJ systems biology and applications
|November 28, 2024
概括
一个新的深度学习模型预测了COVID-19抗体如何中和SARS-CoV-2变种. 这种工具有助于开发更好的抗体治疗方法来对抗像Omicron亚型病毒这样的不断发展的病毒.
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:
- 开发有效的抗体治疗方法来对抗像SARS-CoV-2这样快速演变的病毒是具有挑战性的.
- 传统的病毒变体中和试验是耗时且范围有限的.
研究的目的:
- 创建一个深度学习模型,用于预测针对SARS-CoV-2变种中和抗体活性的变化.
- 评估新兴变种对COVID-19治疗药物和疫苗疗效的影响.
主要方法:
- 利用了67,885个SARS-CoV-2尖端序列和7,069个体外中和化试验的数据集.
- 开发并验证了一种深度学习模型,用于预测中和活动中的折叠变化.
- 应用该模型来预测针对新SARS-CoV-2血统的抗体活性.
主要成果:
- 深度学习模型在预测中和活动 (R2 = 0.77) 时取得了很高的准确性.
- 预计对EG.5,FL.1.5.1和XBB.1.16.1等新变种的抗体活性显著降低.
- 确定F456L尖端突变是降低抗体疗效的关键因素.
结论:
- 深度学习提供了一种强大的方法来预测SARS-CoV-2变种的抗体中和.
- 新兴变种,特别是XBB后代,对当前抗体的敏感性降低.
- 在最近的SARS-CoV-2变种中,F456L突变是抗体逃逸的关键决定因素.
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