IgPose:一个生成数据增强管道,用于强大的免疫球蛋白-抗原结合预测
Tien-Cuong Bui1, Injae Chung1, Wonjun Lee1
1Arontier Co., Ltd, Republic of Korea 241 Gangnam-daero, 06735, Seoul.
Bioinformatics (Oxford, England)
|February 15, 2026
概括
IgPose是一个新的计算框架,可以准确预测免疫球蛋白-抗原 (Ig-Ag) 结合姿势和得分. 它使用生成管道解决了数据稀缺问题,并实现了强大的性能,有助于抗体发现.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 免疫信息学是指免疫信息学.
背景情况:
- 预测免疫球蛋白-抗原 (Ig-Ag) 结合是具有挑战性的,因为实验数据有限,结构预测不准确.
- 准确预测Ig-Ag复合体对于抗体的发现和工程至关重要.
研究的目的:
- 为了开发一个可通用的框架,IgPose,用于准确的Ig-Ag姿势识别和评分.
- 通过一种新的生成数据增强管道来解决Ig-Ag复合体预测中的数据稀缺问题.
主要方法:
- 使用合成诱构建了结构性免疫球蛋白诱数据库 (SIDD),以减轻数据稀缺.
- 集成的等价图神经网络,ESM-2嵌入式和封闭的循环单元,以捕捉几何和进化特征.
- 实现了以接口为重点的k-hop采样和生物指导的聚合,以提高概括性.
主要成果:
- IgPose框架包括IgPoseClassifier用于姿势歧视和IgPoseScore用于DockQ得分估计.
- 在内部测试集和CASP-16基准测试中取得了强的表现,表现优于现有的基线.
- 证明了准确的姿势过和排名能力,用于高通量抗体发现.
结论:
- IgPose提供了一种通用的计算工具,用于推进抗体发现管道.
- 该框架的生成方法和集成的深度学习模型提高了Ig-Ag结合预测的准确性.
- IgPose促进了对潜在抗体候选者的有效识别和评分.
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