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蛋白质语言模型暴露了病毒免疫模仿

Dan Ofer¹, Michal Linial¹

¹Department of Biological Chemistry, Life Science Institute, Faculty of Science and Mathematics, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

|September 27, 2025

概括

这项研究使用蛋白质语言模型 (PLMs) 来识别通过分子模拟逃避宿主免疫力的病毒蛋白质. 机器学习模型,就像免疫系统一样,难以分类表现出低免疫性和模仿宿主特征的病毒蛋白.

科学领域:

计算病毒学计算病毒学.
免疫信息学是指免疫信息学.
机器学习在生物学中的应用

背景情况:

病毒使用分子模仿来逃避宿主免疫反应,使免疫识别和治疗干预复杂化.
区分病毒和宿主蛋白质对于理解病毒病原体和开发有效的对策至关重要.
现有的方法在准确识别病毒蛋白质方面面临挑战,特别是那些参与免疫逃避的病毒蛋白质.

研究的目的:

开发和验证机器学习模型,使用预训练的蛋白质语言模型 (PLM) 来区分病毒和人类蛋白质.
识别和描述模型经常错误分类的病毒蛋白,表明潜在的免疫逃避策略.
通过将PLM与可解释的AI技术相结合,获得对病毒免疫逃脱的机制性见解.

主要方法:

利用预训练的蛋白质语言模型 (PLM) 来基于病毒和人类蛋白质的序列分类.
将PLM与可解释的AI模型集成在一起,以解释驱动蛋白质分类和错误分类的特征.
使用曲线下的接收器操作特征面积 (ROC-AUC) 评估模型性能,并分析错误分类的序列.

主要成果:

开发的模型实现了最先进的性能,ROC-AUC为99.7%.
错误分类的病毒蛋白 (3.9%的序列) 具有较低的免疫性,属于与慢性感染相关的人类特异性病毒家族.
分析揭示了重叠的生物物理信号,使免疫系统和机器学习模型在识别免疫逃避病毒蛋白中产生混.

结论:

将PLM与可解释的AI结合起来,为计算病毒学和理解病毒免疫逃生机制提供了一种强大的方法.
这些发现凸显了病毒分子模拟和低免疫性蛋白质对自然免疫力和人工智能构成的挑战.
这项研究为合理设计疫苗和策略,以对抗持久性和致病性病毒感染提供了影响.

关键词:

在 IL-10 中,IL-10 是 IL-10 的代表.在 PLM PLM 中.蛋白质BERT是什么蛋白质BERT是什么适应性免疫系统适应性免疫系统自身免疫性疾病自身免疫性疾病深度学习是一种深度学习.标志性标志性标志性标志性功能选择功能选择免疫学耐受性免疫学耐受性

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