使用蛋白质语言模型对亲病毒性SARS-CoV-2宿主因子的TransFactor预测
Yang An1,2, Valter Bergant3,4, Samuele Firmani1,2
1Computational Health Center, Helmholtz Center Munich, Neuherberg 85764, Germany.
Bioinformatics (Oxford, England)
|September 10, 2025
概括
TransFactor是一种新的计算工具,使用蛋白质序列预测抗病毒药物开发的关键宿主蛋白. 这一框架有助于识别病毒点,增强疫情防控能力.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 机器学习是机器学习.
背景情况:
- 流行病突显了理解病毒病原学的差距.
- 需要快速识别抗病毒标的宿主因素.
- 实验数据的生成是耗时且异质的.
研究的目的:
- 介绍TransFactor,一个用于预测和优先考虑宿主因素的计算框架.
- 为了利用蛋白质语言模型来识别宿主因素.
- 为了指导抗病毒疗法开发.
主要方法:
- 使用预先训练的ESM-2蛋白语言模型.
- 从33个SARS-CoV-2研究中对实验确定的主因进行了微调模型.
- 为了解释性,采用了计算性氨酸扫描.
主要成果:
- 超越现有的机器和深度学习模型.
- 确定了亲病毒蛋白域 (例如,COMM,PX,RRM).
- 预测与已知的宿主因子的基因本体学丰富相一致.
结论:
- 基于变压器的模型可以提升宿主因子预测.
- 转因子为识别抗病毒目标提供了一个框架.
- 提高对病毒威胁和未来流行病的准备能力.
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