Sun Chuance, Li Xiangyi, Xu Honglin, Tang Yike, Bai Ganggang, Wang Yanjing, Ma Buyong
Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University Shanghai 200240 China
Shanghai Digiwiser Biotechnology, Limited Shanghai 201203 China.
Chem Sci. 2025 Aug 12. doi: 10.1039/d5sc03707g.
Predicting Antibody-Antigen (Ab-Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset. In nanobody-antigen structure prediction, SAGERank, coupled with a protein dynamics structure prediction algorithm, slightly outperforms Alphafold3. Most importantly, our study demonstrates the real potential of inductive deep learning to overcome the small dataset problem in molecular science. The SAGERank models trained for antibody-antigen docking can be used to examine general protein-protein interaction tasks, such as T Cell Receptor-peptide-Major Histocompatibility Complex (TCR-pMHC) recognition, classification of biological crystal interfaces, and prediction of ternary complexes of molecular glues. In the cases of ranking docking decoys and identifying biological interfaces, SAGERank is competitive with or outperforms state-of-the-art methods.
预测抗体 - 抗原（Ab - Ag）对接以及基于结构的设计是计算生物学中重大的长期挑战，且在治疗方面具有重要意义。我们提出了SAGERank，这是一个使用图采样和聚合网络进行抗体设计的通用、可配置的深度学习框架。SAGERank成功预测了癌症靶点数据集中的大多数表位。在纳米抗体 - 抗原结构预测中，SAGERank与蛋白质动力学结构预测算法相结合，略优于Alphafold3。最重要的是，我们的研究证明了归纳深度学习在克服分子科学中小数据集问题方面的真正潜力。为抗体 - 抗原对接训练的SAGERank模型可用于检查一般的蛋白质 - 蛋白质相互作用任务，如T细胞受体 - 肽 - 主要组织相容性复合体（TCR - pMHC）识别、生物晶体界面分类以及分子胶水三元复合物的预测。在对接诱饵排序和识别生物界面的情况下，SAGERank与最先进的方法具有竞争力或更胜一筹。
