多SAAl:使用多尺度深度学习对抗体相互作用的序列信息预测
Zexin Lv1, Dongliang Hou1, Minghua Hou1
1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Journal of chemical information and modeling
|August 28, 2025
概括
使用序列数据,MultiSAAI准确预测抗体与抗原的相互作用,其性能优于现有的方法. 这种计算框架通过模拟复杂的结合机制来帮助高吞吐量发现治疗抗体.
科学领域:
- 生物化学
- 计算生物学
- 免疫学
背景情况:
- 对抗体-抗原相互作用的预测对于开发新疗法至关重要,但通常受到实验成本和基于静态结构的方法无法捕获动态结合的限制.
- 需要精确的基于序列的预测方法来克服基于结构的方法在建模对抗体-抗原结合至关重要的动态形状变化方面的局限性.
研究的目的:
- 开发和验证MultiSAAI,一个用于预测抗体-抗原相互作用的新型序列信息计算框架.
- 显式建模抗体重链和轻链在抗原结合中的不同贡献.
- 通过先进的计算预测提高治疗抗体发现的准确性和效率.
主要方法:
- 多SAAI集成了语言模型嵌入,物理化学性质,几何约束和残留可替代性来描述相互作用.
- 使用多尺度网络架构来评估全球残留对兼容性和局部氨基酸适应性.
- 该框架包含特定地点的信息和具有生物基础的约束原则,以反映实际的相互作用机制.
主要成果:
- 在通用抗体- 抗原相互作用数据集上,MultiSAAI的AUROC得分为0. 772.
- 该模型在SARS-CoV-2数据集上表现出卓越的性能,AUROC得分为0.947.
- 在基准测试中,MultiSAAI的表现优于A2binder和AbAgIntPre.
结论:
- MultiSAAI提供了基于序列的强大方法来预测抗体-抗原相互作用,超越了当前的方法.
- 该框架能够建模动态相互作用并结合生物原理,从而提高其预测准确性.
- 多SAAI显示出加速高通量治疗抗体发现的巨大潜力.
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