AttABseq:一种基于注意力的深度学习预测方法,用于基于蛋白质序列的抗原-抗体结合亲和力变化
Ruofan Jin1,2, Qing Ye1, Jike Wang1
1College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China.
Briefings in bioinformatics
|July 3, 2024
概括
一个新的深度学习模型,AttABseq,准确地预测了抗体突变对结合亲和力的影响. 这种人工智能方法加速了治疗抗体的优化,优于传统方法,并提供了对突变影响的见解.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 传统的抗体优化方法 (混合瘤,菌体显示) 是缓慢而昂贵的.
- 计算和人工智能方法正在出现,以加强治疗性抗体的开发.
研究的目的:
- 开发一个端到端的,基于序列的深度学习模型,用于预测由抗体突变引起的抗原-抗体结合亲和力变化.
- 创建一个工具,加速和改进治疗抗体的优化.
主要方法:
- 开发了基于注意力的深度学习模型AttABseq,使用多种抗原-抗体复杂序列作为输入.
- 与抗体残留突变相关的预测结合亲和力变化.
- 在三个基准数据集上评估模型性能.
主要成果:
- AttABseq的准确性比现有的基于序列的模型 (皮尔森相关系数) 高出120%.
- AttABseq的性能与基于结构的方法相提并论,甚至比它们更好.
- 该模型在各种抗原-抗体复合体和突变场景中显示出强大的概括,即使没有结构数据.
结论:
- AttABseq提供了一种高度准确和高效的方法来预测抗体突变对结合亲和力的影响.
- 该模型的可解释性有助于在残留水平上可视化突变影响,促进自动化抗体序列优化.
- AttABseq为促进治疗性抗体开发提供了具有竞争力的解决方案.
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