Wu Jun, Kong Xiangzhe, Sun Ningguan, Wei Jing, Shan Sisi, Feng Fuli, Wu Feng, Peng Jian, Zhang Linqi, Liu Yang, Ma Jianzhu
University of Science and Technology of China, Hefei, China.
Department of Computer Science and Technology, Tsinghua University, Beijing, China; Institute for AI Industry Research, Tsinghua University, Beijing, China.
Cell Syst. 2025 Jun 18;16(6):101270. doi: 10.1016/j.cels.2025.101270. Epub 2025 Apr 29.
Designing antibodies with desired binding specificity and affinity is essential for pharmaceutical research. While diffusion-based models have advanced the co-design of the complementarity-determining region (CDR) sequences and structures, challenges remain, including non-informative priors, incompatibility with discrete amino acid types, and impractical computational costs in large-scale sampling. To address these, we propose FlowDesign, a sequence-structure co-design approach via flow matching, offering (1) flexible prior selection, (2) direct matching of discrete distributions, and (3) enhanced efficiency for large-scale sampling. By leveraging various priors, data-driven structural models proved the most informative. FlowDesign outperformed baselines in amino acid recovery (AAR), root-mean-square deviation (RMSD), and Rosetta energy. We also applied FlowDesign to design antibodies targeting the HIV-1 receptor CD4. FlowDesign yielded antibodies with improved binding affinity and neutralizing potency compared with the antibody ibalizumab across multiple HIV mutants, validated by biolayer interferometry (BLI) and pseudovirus neutralization. This highlights FlowDesign's potential in antibody and protein design. A record of this paper's transparent peer review process is included in the supplemental information.
设计具有所需结合特异性和亲和力的抗体对于药物研究至关重要。虽然基于扩散的模型推动了互补决定区（CDR）序列和结构的共同设计，但挑战依然存在，包括无信息先验、与离散氨基酸类型不兼容以及大规模采样中不切实际的计算成本。为了解决这些问题，我们提出了FlowDesign，一种通过流匹配进行序列 - 结构共同设计的方法，它具有以下特点：（1）灵活的先验选择；（2）离散分布的直接匹配；（3）大规模采样的更高效率。通过利用各种先验，数据驱动的结构模型被证明是最具信息性的。FlowDesign在氨基酸回收率（AAR）、均方根偏差（RMSD）和Rosetta能量方面优于基线。我们还应用FlowDesign设计针对HIV - 1受体CD4的抗体。与抗体ibalizumab相比，FlowDesign产生的抗体在多个HIV突变体上具有更高的结合亲和力和中和效力，这通过生物层干涉术（BLI）和假病毒中和得到验证。这突出了FlowDesign在抗体和蛋白质设计中的潜力。本文透明同行评审过程的记录包含在补充信息中。
