通过贝叶斯主动学习和生物物理学预测高适应性病毒蛋白质变体
Marian Huot1,2, Dianzhuo Wang1,3, Jiacheng Liu4
1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
概括
早期检测高适应性病毒变种至关重要. 一个新的积极学习框架,VIRAL,加快了识别的五倍,使得迅速的流行病反应.
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准备至关重要.
- 有限的实验资源往往阻碍了新兴变异的及时识别.
- 预测病毒健康和进化轨迹对于有效的公共卫生干预至关重要.
研究的目的:
- 引入VIRAL (通过快速主动学习进行病毒识别),这是一个主动学习框架,用于快速识别高适应性病毒变异.
- 通过计算预测和有限的实验验证,加快有关病毒变异的发现.
- 开发一个早期预警系统,对潜在的流行病病毒进行预警.
主要方法:
- 蛋白质语言模型,高斯过程与不确定性估计以及生物物理模型的整合.
- 应用少数射击学习来预测新型变体适应性.
- 与历史SARS-CoV-2数据和随机抽样策略进行基准测试.
主要成果:
- 与随机抽样相比,VIRAL可以加快高适应性变异的识别速度高达五倍.
- 该框架要求对不到1%的可能变体进行实验性表征.
- 识别了经常发生突变的部位,预测了抗体逃逸和ACE2结合的保存,提前长达两年.
- 不确定性驱动的变体选择促进了进化上遥远,潜在的危险变体的发现.
结论:
- VIRAL提供了一种计算高效和有效的方法,用于早期检测高适应性病毒变体.
- 该框架可以作为SARS-CoV-2和其他具有流行病潜力的新兴病毒的早期预警系统.
- 将预测建模与主动学习相结合,大大提高了变种监测的速度和范围.
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