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Updated: Jan 10, 2026

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具有结构特征的图表注意力提高了在蛋白质接口上识别功能序列的概括性

J Ash¹, I M Francino-Urdaniz², S P Kells²

¹Department of Chemistry & Chemical Biology, Rutgers The State University of New Jersey, 123 Bevier Rd, Piscataway, NJ 08854, United States of America.

bioRxiv : the preprint server for biology

|November 26, 2025

概括

预测蛋白质接口兼容性是一项挑战. 结合结构和语言嵌入的新图表注意力模型显著提高了各种蛋白质变体的预测准确性,有助于理解传染病和设计治疗方法.

科学领域:

计算生物学计算生物学
蛋白质工程是指蛋白质工程.
结构生物学结构生物学

背景情况:

预测蛋白质-蛋白质接口序列兼容性是生物学中的一个关键挑战.
现有的基于序列的模型很难将其推广到与其远距离相关的蛋白质序列.

研究的目的:

通过整合深度学习蛋白质模型来提高蛋白质接口预测的通用性.
开发和验证一种新的深度学习架构,用于预测功能蛋白质变体.

主要方法:

设计和选了SARS-CoV-2尖端受体结合域 (RBD) 的深度突变库,用于ACE2受体结合.
开发了一个图表注意网络 (GAN-PLM),结合了蛋白质结构图表和蛋白质语言模型 (PLM) 嵌入.
将GAN-PLM性能与基线监督学习和序列嵌入模型进行比较.

主要成果:

创建了一个超过43,000个SARS-CoV-2 RBD变体的数据集,探索了一个扩展的序列空间.
纯粹基于序列的模型显示,对未见的变体的概括性很差.
开发的GAN-PLM模型在预测跨多种序列的功能性ACE2结合变异方面显著超过了基线模型.

结论:

将基于结构和序列的特性集成到深度学习模型中,可以提高蛋白质接口功能的预测概括性.
GAN-PLM方法为了解和设计蛋白相互作用提供了一个强大的工具.
这对传染病研究和治疗蛋白质设计具有广泛的影响.

关键词:

生物科学生物科学生物物理和计算生物学能源特征能源特征能源特征可以概括的概括性图表注意力网络图表注意力网络蛋白质接口的接口是蛋白质.序列变化的变化.零射击预测的预测.

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