Li Junxin, Zhang Chao, Xia Wei, Kan Hei Wun, Huang Kaifang, Li Sai, Ige Mark Akinola, Yu Qiuliyang, Zhao Jiawei, Wan Xiaochun, Zhang John Z H, Zhang Haiping
Center for Protein and Cell-based Drugs, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China.
Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen 518055, China.
Brief Bioinform. 2025 Aug 31;26(5). doi: 10.1093/bib/bbaf445.
Enhancing antibody affinity is a critical goal in antibody design, as it improves therapeutic efficacy, specificity, and safety while reducing dosage requirements. Traditional methods, such as single-point mutations or combinatorial mutagenesis, are limited by the impracticality of exhaustively exploring the vast mutational space. To address this challenge, we developed a novel computational pipeline that integrates evolutionary constraints, antibody-antigen-specific statistical potentials, molecular dynamics simulations, metadynamics, and a suite of deep learning models to identify affinity-enhancing mutations. Our deep learning framework includes MicroMutate, which predicts microenvironment-specific amino acid mutations, and graph-based models that evaluate postmutation antigen-antibody-binding probabilities. Using this approach, we screened 12 single-point mutant antibodies targeting the hemagglutinin of the H7N9 avian influenza virus, starting from antibodies with initial affinities in the subnanomolar range, with one showing a 4.62-fold improvement. To demonstrate the generalizability of our method, we applied it to engineer an antibody against death receptor 5 with initial affinities in the subnanomolar range, successfully identifying a mutant with a 2.07-fold increase in affinity. Our work underscores the transformative potential of integrating deep learning and computational methods for rapidly and precisely discovering affinity-enhancing mutations while preserving immunogenicity and expression. This approach offers a powerful and universal platform for advancing antibody therapeutics.
提高抗体亲和力是抗体设计中的一个关键目标，因为它可以提高治疗效果、特异性和安全性，同时降低剂量需求。传统方法，如单点突变或组合诱变，受到难以详尽探索巨大突变空间的限制。为应对这一挑战，我们开发了一种新颖的计算流程，该流程整合了进化约束、抗体-抗原特异性统计势、分子动力学模拟、元动力学以及一系列深度学习模型，以识别增强亲和力的突变。我们的深度学习框架包括预测微环境特异性氨基酸突变的MicroMutate，以及评估突变后抗原-抗体结合概率的基于图的模型。使用这种方法，我们从初始亲和力在亚纳摩尔范围内的抗体开始，筛选了12种针对H7N9禽流感病毒血凝素的单点突变抗体，其中一种显示出4.62倍的亲和力提升。为证明我们方法的通用性，我们将其应用于设计一种针对死亡受体5的抗体，该抗体初始亲和力在亚纳摩尔范围内，成功鉴定出一种亲和力提高2.07倍的突变体。我们的工作强调了整合深度学习和计算方法在快速、精确发现增强亲和力的突变同时保留免疫原性和表达方面的变革潜力。这种方法为推进抗体治疗提供了一个强大而通用的平台。
