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关于SARS-CoV-2突变影响的机器学习尖端 RBD 关于与人类ACE2结合的亲和力基于深度突变扫描数据

Hui Xia^1,2, Dacong Wei², Zhihong Guo¹

¹Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.

|August 14, 2025

概括

机器学习模型使用深度突变扫描数据预测SARS-CoV-2变种突变. 这些模型,包括双编码CNN,准确评估突变对HACE2结合的尖端蛋白的影响,有助于病毒监测.

科学领域:

病毒学病毒学
生物化学生物化学
计算生物学计算生物学

背景情况:

严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 在尖端受体结合域 (RBD) 中积累了突变.
这些突变可以改变对人类血管激素转化酶2 (hACE2) 受体的结合亲和力,影响病毒的传播能力.
深度突变扫描 (DMS) 是评估突变对蛋白质功能影响的关键实验方法.

研究的目的:

开发和评估机器学习 (ML) 模型,以预测SARS-CoV-2突变对尖端蛋白功能的影响.
通过结合当地环境信息来改进基于物理的模型.
评估双编码卷积神经网络 (CNN) 模型与现有的蛋白质语言模型的性能.

主要方法:

使用SARS-CoV-2 DMS数据构建了ML模型,输入的特征来自Rosetta计算的能量术语和本地残留环境信息.
采用CNN模型利用氨基酸序列,物理化学和生物化学特性.
应用转移学习,为特定的SARS-CoV-2变种 (阿尔法,三角形,欧米克朗亚型) 微调CNN模型.

主要成果:

ML模型与实验DMS数据显示出很好的一致性.
双编码CNN模型在多个DMS数据集上表现优于三种流行的蛋白质语言模型.
精心调整的CNN模型成功预测了变体特异性影响,包括Omicron亚变体.

结论:

在DMS数据上训练的ML模型可以预测单点和多点突变的影响.
这些模型为病毒监测和了解SARS-CoV-2演变提供了宝贵的见解.
双编码CNN模型为DMS研究提供了强大的,替代的ML方法,而不需要3D结构信息.

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