使用机器学习来检测可能感染人类的冠状病毒
Georgina Gonzalez-Isunza1, M Zaki Jawaid2, Pengyu Liu1
1Department of Microbiology and Molecular Genetics, University of California, Davis, CA, USA.
Scientific reports
|June 8, 2023
概括
科学家们开发了一种人工神经网络,以预测动物冠状病毒是否可以感染人类. 该模型通过分析尖端蛋白序列来识别潜在的动物传播威胁,有助于早期检测和监测新型病毒.
科学领域:
- 病毒学 病毒学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 识别新型病毒的宿主范围对于疫情准备至关重要.
- 冠状病毒,特别是α和β型,由于它们具有跨物种传播的潜力,构成重大威胁.
研究的目的:
- 开发一种预测模型,用于识别可能感染人类的动物冠状病毒.
- 评估机器学习在预测病毒宿主扩张事件中的实用性.
主要方法:
- 一个人工神经网络被训练在尖端蛋白序列和宿主受体结合数据的α和β冠状病毒.
- 开发了一个人类结合潜力 (h-BiP) 评分来量化对人类受体的结合亲和力.
- 用分子动力学模拟来分析特定已识别的病毒的结合特性.
主要成果:
- 该模型准确地区分了冠状病毒之间的结合潜力,产生了人类结合潜力 (h-BiP) 评分.
- 确定了三种以前未知的与人类结合的冠状病毒:蝙蝠冠状病毒BtCoV/133/2005,与HKU5相关的蝙蝠冠状病毒Pipistrellus abramus和Rhinolophus affinis冠状病毒分离物LYRa3.
- 该模型成功预测了SARS-CoV-2与人类受体的结合,即使在接受了SARS-CoV-2前数据的训练时也是如此.
结论:
- 机器学习,特别是人工神经网络,是预测病毒宿主扩张和识别潜在动物病毒威胁的强大工具.
- 开发的h-BiP评分和模型可以帮助监测新型冠状病毒,以评估大流行风险.
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