De novo design of epitope-specific antibodies via a structure-driven computational workflow

作者：Fandi Wu, Yu Zhao, Jiaxiang Wu, Biaobin Jiang, Bing He, Long-Kai Huang, Chenchen Qin, Yang Xiao, Fan Yang, Rubo Wang, Ningqiao Huang, Huaxian Jia, Yuyi Liu, Houtim Lai, Tingyang Xu, Fang Wang, Zihan Wu, Yidong Song, Shaoning Li, Wei Liu, Yu Rong, Peilin Zhao, Jianhua Yao · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-67361-9 · 被引用次数：10 · 研究领域：Monoclonal and Polyclonal Antibodies Research、vaccines and immunoinformatics approaches、Biochemical and Structural Characterization

Accurate modeling of antibody-antigen complex structures holds significant potential for advancing biomedical research and the design of therapeutic antibodies. Compared to general proteins, progress in antibody structure prediction and design has been slow, and antibody discovery is still based on time-consuming animal immunization or library screening methods. Here, we present tFold System, a high-throughput computational workflow that integrates antibody structure prediction (tFold-Ab), antibody-antigen complex modeling (tFold-Ag), structure-guided virtual screening, and de novo epitope-specific antibody design. Using this system, we de novo design monoclonal antibodies (mAbs) against four therapeutically relevant antigens: influenza hemagglutinin (Flu A), PD-1, PD-L1, and SARS-CoV-2 RBD (SC2RBD). Experimental validation by surface plasmon resonance (SPR) following high-throughput screening via phage display shows the designed antibodies achieve nanomolar binding affinities and precise epitope targeting, demonstrating the efficiency of the integrated computational-experimental pipeline. Our results demonstrate that tFold System overcomes key limitations of existing methods by enabling rapid, high-throughput antibody discovery against user-defined epitopes.