通过深度学习预测SARS-COV-2的抗原演变
Wenkai Han1,2, Ningning Chen1,2, Xinzhou Xu3,4
1Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
Nature communications
|June 13, 2023
概括
机器学习引导的抗原进化预测 (MLAEP) 预测SARS-CoV-2的进化和免疫逃避. 这种工具有助于开发更好的疫苗,并通过预测抗原变化,为未来的变种做准备.
科学领域:
- 病毒学 病毒学
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- SARS-CoV-2 持续演变,通过逃避疫苗诱导和自然免疫,构成公共卫生威胁.
- 预测抗原变化对于有效的公共卫生策略至关重要,但由于病毒的大量序列多样性而复杂.
研究的目的:
- 开发一种计算工具,MLAEP,用于预测SARS-CoV-2抗原进化和病毒适应性.
- 识别具有增强免疫逃避能力的新突变和新兴变体.
主要方法:
- MLAEP 集成了结构建模,多任务学习和基因算法,用于 in silico 定向进化.
- 该模型分析了现有的SARS-CoV-2变种,以推断抗原进化轨迹并预测未来的变化.
- 使用体外中和抗体结合试验验证了预测.
主要成果:
- MLAEP准确地预测了与采样时间相关的抗原轨迹沿着现有的SARS-CoV-2变异的顺序.
- 该方法在免疫受损患者中发现了新的突变,以及像XBB.1.5.5这样的新兴变异.
- 预测的变种在实验分析中显示显著增强了免疫逃避.
结论:
- MLAEP提供了一种强大的方法来理解和预测病毒抗原进化.
- 这种工具可以指导疫苗的开发,并改善对未来的SARS-CoV-2变种和其他快速演变的病原体的准备.
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