从单个序列到进化轨迹:蛋白质语言模型捕捉了SARS-CoV-2的进化潜力
Kieran D Lamb1,2, Joseph Hughes1, Spyros Lytras1,3
1MRC-University of Glasgow Centre for Virus Research, School of Infection and Immunity, Glasgow, UK.
Nature communications
|February 19, 2026
概括
蛋白质语言模型 (PLMs) 分析蛋白质序列,以预测突变效应,而不需要多个序列对齐. 这些模型捕捉了进化史和变体特征,有助于理解病毒病原体.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 结构生物学是结构生物学.
背景情况:
- 蛋白质语言模型 (PLM) 利用自然语言处理概念来解释蛋白质序列.
- PLM可以从单独的氨基酸序列推断蛋白质结构和功能,绕过多重序列对齐 (MSA) 的需要.
研究的目的:
- 调查PLM表示,特别是ESM-2,如何评估突变诱导的蛋白质变异.
- 评估未经修改,预先训练的PLM在SARS-CoV-2的变异效应预测中的实用性.
主要方法:
- 在Silico深度突变扫描 (DMS) 用ESM-2 PLM对SARS-CoV-2尖端蛋白进行了.
- 评估了ESM-2直接从序列上下文中捕捉进化约束的能力.
- 该模型的性能与需要蛋白质结构或多个序列的方法进行了比较.
主要成果:
- ESM-2成功地从序列上下文捕获了进化约束,类似于基于MSA的方法.
- ESM-2表示编码了SARS-CoV-2变种的进化轨迹,并确定了令人担忧的变种的独特特征.
- 该模型展示了预测受体结合和抗原性转变的能力,并识别了表皮性相互作用.
结论:
- 像ESM-2这样的未经修改的预先训练的PLM是预测变异效应的强大工具,包括未观察到的突变.
- ESM-2提供了对病毒进化和病原体特征的洞察,适用于新型病毒病原体和任何蛋白质序列.
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