HAIRpred:从其主要结构预测抗原中的人类抗体相互作用残留物
Ruchir Sahni1,2, Nishant Kumar1, Gajendra P S Raghava1
1Department of Computational Biology, Indraprastha Institute of Information Technology Delhi, New Delhi, India.
Protein science : a publication of the Protein Society
|July 17, 2025
概括
这项研究引入了一种用于预测人类特异性B细胞表位的新方法,其性能优于现有的方法. 开发的HAIRpred工具增强了科学界对抗体-抗原相互作用的预测.
科学领域:
- 免疫信息学是指免疫信息学.
- 计算生物学 计算生物学
- 机器学习在免疫学中的应用
背景情况:
- 预测B细胞表位的现有方法缺乏宿主特异性.
- 对抗体-抗原复合物的分析强调了对宿主特异性表位预测的需要.
研究的目的:
- 开发一种新的宿主特异性方法来预测构造性B细胞表位.
- 专注于抗原中的人类抗体相互作用残留物,以进行人类特异性预测.
主要方法:
- 机器学习模型在277个人类抗体-抗原复合体上进行了训练和评估.
- 使用一热编码,进化概况,蛋白质语言模型嵌入和相对溶剂可访问性进行比较的预测性能.
- 开发了一种组合模型,将进化概况和相对表面可访问性结合起来.
主要成果:
- 组合模型实现了0.72.7的接收器操作特征曲线 (AUROC) 下的最高面积.
- 进化概况和预测的表面相对溶剂可访问性单独提高了AUROC至0.67.
- 与现有方法相比,开发的方法在独立数据集上表现出优异的性能.
结论:
- 这种新的方法有效地预测了人类特异性的构造性B细胞表征.
- 组合模型结合进化配置文件和相对表面可访问性提供了最佳性能.
- 科学界可以使用一个名为HAIRpred的网络服务器来预测与人类抗体相互作用的残留物.
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