Zhang Kaiwen, Tao Yuhao, Wang Fei
Research Center for Social Intelligence, Fudan University, Handan Street, Shanghai 200433, China.
School of Computer Science and Technology, Fudan University, Handan Street, Shanghai 200433, China.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf008.
Antibodies play a key role in medical diagnostics and therapeutics. Accurately predicting antibody-antigen binding is essential for developing effective treatments. Traditional protein-protein interaction prediction methods often fall short because they do not account for the unique structural and dynamic properties of antibodies and antigens. In this study, we present AntiBinder, a novel predictive model specifically designed to address these challenges. AntiBinder integrates the unique structural and sequence characteristics of antibodies and antigens into its framework and employs a bidirectional cross-attention mechanism to automatically learn the intrinsic mechanisms of antigen-antibody binding, eliminating the need for manual feature engineering. Our comprehensive experiments, which include predicting interactions between known antigens and new antibodies, predicting the binding of previously unseen antigens, and predicting cross-species antigen-antibody interactions, demonstrate that AntiBinder outperforms existing state-of-the-art methods. Notably, AntiBinder excels in predicting interactions with unseen antigens and maintains a reasonable level of predictive capability in challenging cross-species prediction tasks. AntiBinder's ability to model complex antigen-antibody interactions highlights its potential applications in biomedical research and therapeutic development, including the design of vaccines and antibody therapies for rapidly emerging infectious diseases.
抗体在医学诊断和治疗中发挥着关键作用。准确预测抗体与抗原的结合对于开发有效的治疗方法至关重要。传统的蛋白质-蛋白质相互作用预测方法往往存在不足，因为它们没有考虑抗体和抗原独特的结构和动态特性。在本研究中，我们提出了AntiBinder，这是一种专门设计用于应对这些挑战的新型预测模型。AntiBinder将抗体和抗原独特的结构和序列特征整合到其框架中，并采用双向交叉注意力机制自动学习抗原-抗体结合的内在机制，无需人工进行特征工程。我们的综合实验，包括预测已知抗原与新抗体之间的相互作用、预测未见抗原的结合以及预测跨物种抗原-抗体相互作用，表明AntiBinder优于现有的最先进方法。值得注意的是，AntiBinder在预测与未见抗原的相互作用方面表现出色，并且在具有挑战性的跨物种预测任务中保持了合理的预测能力水平。AntiBinder对复杂抗原-抗体相互作用进行建模的能力突出了其在生物医学研究和治疗开发中的潜在应用，包括针对快速出现的传染病设计疫苗和抗体疗法。
