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深度突变学习预测了SARS-CoV-2受体结合域中的ACE2结合和抗体逃逸
Joseph M Taft1, Cédric R Weber2, Beichen Gao1
1Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland; Botnar Research Centre for Child Health, Basel 4058, Switzerland.
Cell
|September 23, 2022
概括
深度突变学习预测了SARS-CoV-2的演变. 这项技术识别了数十亿种尖端蛋白变种,有助于开发新的COVID-19疫苗和抗体治疗抗性菌株.
科学领域:
- 病毒学
- 免疫学
- 计算生物学
背景情况:
- 随着SARS-CoV-2的持续演变,特别是尖端蛋白的ACE2受体结合域 (RBD) 的突变,导致了对疫苗和抗体有抗性的变体的出现,从而延长了COVID-19的流行.
- 了解这些突变的进化轨迹和潜在影响对于开发有效的对策至关重要.
研究的目的:
- 开发一种机器学习引导的蛋白质工程技术,即深度突变学习 (DML),用于预测SARS-CoV-2 RBD中的组合突变的影响.
- 探索潜在的RBD变异的广泛序列空间,并确定那些对ACE2结合和抗体逃逸有影响的变异.
主要方法:
- 深度突变学习 (DML) 的开发和应用,这是一种机器学习的蛋白质工程方法.
- 通过预测它们对ACE2结合和抗体逃逸的影响,调查数十亿个潜在的受体结合域 (RBD) 变异.
主要成果:
- 识别可能通过各种进化途径出现的SARS-CoV-2变种的多样性.
- 准确预测组合突变对ACE2结合和抗体逃逸在庞大的序列空间的影响.
结论:
- 深度突变学习 (DML) 为预测当前和未来的SARS-CoV-2变种提供了一个强大的工具,包括像Omicron这样的高度突变变种.
- 这项技术可以指导下一代疫苗和治疗抗体治疗的开发,以对抗COVID-19.
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