药物耐药性预测基于一个定向的旗变压器
Dong Chen1, Gengzhuo Liu1, Hongyan Du1
1Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
概括
一个名为CAPTURE的新工具预测了影响PAXLOVID疗效的SARS-CoV-2突变. 它通过分析病毒主要蛋白酶 (Mpro) 突变来识别耐药变异,有助于开发新疗法.
科学领域:
- 病毒学 病毒学
- 药物发现 药物发现 药物发现
- 计算生物学 计算生物学
背景情况:
- SARS-CoV-2 病毒继续演变,构成严重的公共卫生威胁.
- 对像PAXLOVID (nirmatrelvir) 这样的抗病毒药物的新兴耐药性是一个越来越令人担忧的问题.
- 病毒主要蛋白酶 (Mpro) 是尼尔马特雷尔维尔的关键标,突变可以赋予耐药性.
研究的目的:
- 开发一个计算框架来预测SARS-CoV-2 Mpro突变对尼尔马特里尔维尔结合的影响.
- 为了识别和评估耐药的SARS-CoV-2变种.
- 建立实时病毒监测系统,指导下一代治疗药物的开发.
主要方法:
- 开发CAPTURE (用于药物耐药性预测的定向拉普拉西安变压器),这是一个整合突变分析和耐药性预测的工具.
- 使用DFFormer-seq,一个组合模型,结合了定向旗变压器和序列嵌入用于阻力预测.
- 从5月到2022年12月对Mpro突变进行分析,以追踪抗性演变.
主要成果:
- 观察到Nirmatrelvir结合部位附近的Mpro突变的频率越来越高,这表明PAXLOVID使用导致的进化加速.
- 确定了几种潜在的耐药突变,包括经过实验验证的H172Y和F140L,以及其他五种正在等待确认.
- 在Mpro突变者数据上,Capture在预测耐药突变方面表现出57%的回忆率和71%的精度.
结论:
- 该研究提出CAPTURE作为一个强大的框架,用于预测SARS-CoV-2的抗病毒药物耐药性.
- 研究结果强调需要持续的病毒监测,以监测耐药菌株的出现.
- 开发的方法可以为未来抗病毒疗法的设计提供信息,这些疗法可以有效地对抗不断演变的病毒变体.
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