通过贝叶斯主动学习和生物物理学检测少量病毒变异
Marian Huot1,2, Dianzhuo Wang1,3, Jiacheng Liu4
1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA.
bioRxiv : the preprint server for biology
|March 31, 2025
概括
这项研究引入了一个主动学习框架,以快速检测高适应性病毒变异,加快识别的五倍. 它优先进行实验测试,及早识别危险变种,以便为流行病做好准备.
科学领域:
- 病毒学 病毒学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 早期检测高适应性病毒变体对于疫情应对至关重要.
- 有限的实验资源往往会阻碍及时识别新兴变异.
研究的目的:
- 开发一种积极的学习框架,以快速,短时间地预测病毒变体的适应性.
- 在广泛传播之前,加快识别包括SARS-CoV-2在内的相关病毒变体.
主要方法:
- 一个蛋白质语言模型 (ESM3) 的整合,高斯过程与不确定性估计,和一个生物物理模型.
- 使用有限的实验数据来预测变体适应性的Few-shot学习方法.
- 基于历史SARS-CoV-2数据和深度突变扫描的基准测试.
主要成果:
- 与随机抽样相比,高适应性变体的加速识别高达五倍.
- 将实验性表征减少到可能变异的不到1%.
- 确定了与抗体逃逸相关的关键突变部位,并保留了ACE2结合,具有两年的预测优势.
结论:
- 积极学习框架作为一种有效的早期预警系统,用于识别潜在的危险病毒变体.
- 将不确定性纳入变体选择允许对序列格局进行更广泛的探索.
- 该框架可以帮助早期检测具有流行病潜力的新兴病毒.
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