Li Minghui, Shi Yao, Hu Shengqing, Hu Shengshan, Guo Peijin, Wan Wei, Zhang Leo Yu, Pan Shirui, Li Jizhou, Sun Lichao, Lan Xiaoli
School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430000, China.
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btae579.
Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been widely used for this purpose, relying on interfacial amino acids' structural information. Nevertheless, due to technological limitations and high costs of acquiring structural data, the structures of most antigens and antibodies are unknown, and sequence-based methods have gained attention. Existing sequence-based approaches designed for protein-protein affinity prediction exhibit a significant drop in performance when applied directly to antibody-antigen affinity prediction due to imbalanced training data and lacking design in the model framework specifically for antibody-antigen, hindering the learning of key features of antibodies and antigens. Therefore, we propose MVSF-AB, a Multi-View Sequence Feature learning for accurate Antibody-antigen Binding affinity prediction.
MVSF-AB designs a multi-view method that fuses semantic features and residue features to fully utilize the sequence information of antibody-antigen and predicts the binding affinity. Experimental results demonstrate that MVSF-AB outperforms existing approaches in predicting unobserved natural antibody-antigen affinity and maintains its effectiveness when faced with mutant strains of antibodies.
Datasets we used and source code are available on our public GitHub repository https://github.com/TAI-Medical-Lab/MVSF-AB.
准确预测抗原与抗体之间的结合亲和力对于评估治疗性抗体的有效性以及加强抗体工程和疫苗设计至关重要。传统的机器学习方法已广泛用于此目的，这些方法依赖于界面氨基酸的结构信息。然而，由于获取结构数据的技术限制和高成本，大多数抗原和抗体的结构未知，基于序列的方法因此受到关注。现有的用于蛋白质 - 蛋白质亲和力预测的基于序列的方法，由于训练数据不平衡以及模型框架中缺乏专门针对抗体 - 抗原的设计，在直接应用于抗体 - 抗原亲和力预测时性能显著下降，阻碍了对抗体和抗原关键特征的学习。因此，我们提出了MVSF - AB，一种用于准确预测抗体 - 抗原结合亲和力的多视图序列特征学习方法。
MVSF - AB设计了一种融合语义特征和残基特征的多视图方法，以充分利用抗体 - 抗原的序列信息并预测结合亲和力。实验结果表明，MVSF - AB在预测未观察到的天然抗体 - 抗原亲和力方面优于现有方法，并且在面对抗体突变株时仍保持其有效性。
我们使用的数据集和源代码可在我们的公共GitHub存储库https://github.com/TAI - Medical - Lab/MVSF - AB上获取。
