深度几何框架来预测抗体-抗原结合亲和力
Nuwan Bandara1, Dasun Premathilaka2, Sachini Chandanayake2
1Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka; School of Computing and Information Systems, Singapore Management University, Singapore.
Journal of structural biology
|October 25, 2025
概括
这项研究引入了一种新的深度学习框架,用于预测抗体-抗原结合亲和力,通过结合结构和进化数据来提高准确性. 新方法提高了在药物开发中对抗体疗效的预测准确度.
科学领域:
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
- 药物发现 药物发现 药物发现
背景情况:
- 抗体在药物开发中的有效性取决于与向抗原的结合亲和力.
- 传统的结合亲和度量化在计算上是复杂的.
- 当前的深度学习方法往往忽略了蛋白质进化细节和抗原变异.
研究的目的:
- 开发一种更准确,更普遍的深度学习模型,用于抗体-抗原结合亲和力预测.
- 解决现有方法在结构数据质量和进化信息方面的局限性.
- 创建全面的数据集,以推进该领域的数据驱动方法.
主要方法:
- 策划了最大的通用数据集来预测抗体-抗原结合亲和力 (> 100K 序列对, 8K 结构对).
- 提出了一种新的深度几何神经网络,将基于结构和基于序列的模型与交叉注意力机制相结合.
- 利用多层次的层次注意力阻断来建模抗体-抗原相互作用.
主要成果:
- 与最先进的模型相比,平均绝对误差提高了10%.
- 在预测和目标结合亲和值之间显示出强烈的相关性 (>0.87).
- 拟议的框架有效地整合了结构和进化信息.
结论:
- 新的深度几何神经网络在抗体-抗原结合亲和力预测方面取得了重大进展.
- 开发的数据集和框架促进了对抗体设计和药物开发的进一步研究.
- 公开发布数据集和代码支持科学界.
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