OFAntibody：单克隆和双特异性抗体的从头设计
De novo design of monoclonal and bispecific antibodies with OFAntibody
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中文总结 AI 辅助
OFAntibody是一个全原子生成框架，用于单克隆和双特异性抗体的从头设计，通过多组分结构监督和臂感知路由，在纳米抗体和双特异性设计任务中显著提升了候选排序和界面兼容性。
中文摘要 AI 辅助
近年来，生成式蛋白质设计的进展使得能够通过显式靶点和表位条件进行抗体的从头生成。然而，现有的大多数方法仍围绕单一抗原-抗体界面进行构建，而双特异性抗体设计则需要建模多组分复合物，其中多个靶点识别界面必须在一个共享的抗体结构中共存并相互作用。在此，我们提出了OFAntibody，一个用于单克隆和双特异性抗体从头设计的全原子生成框架。OFAntibody通过大规模蒸馏的抗原-抗体复合物扩展了CDR-表位相互作用学习，并引入了多组分结构监督和臂感知的多热点路由，以学习兼容的多界面几何结构，并将每个抗体部分位与其指定的表位耦合。OFAntibody支持跨单克隆抗体和多种双特异性格式（包括串联VHH、双抗体和CODV）的表位条件生成。在纳米抗体设计基准测试中，OFAntibody实现了41.5%的Top-5富集率，相较于RFantibody在竞争性候选排序中提升了5.39倍。在双特异性抗体设计任务中，OFAntibody在三个评估任务中实现了94-100%的热点通过率，并在双抗体、串联VHH和CODV格式中分别实现了60%、13%和8%的能量通过率。OFAntibody还使得相同的靶点组合能够在不同的抗体格式中进行探索，而联合的多界面生成减少了独立设计和事后组装所产生的几何不兼容性。这些结果共同将从头抗体设计从二元抗原-抗体复合物扩展到可编程的多组分复合物，为设计能够结合不同靶点识别及其相关生物学功能的单分子奠定了基础。
英文摘要
Recent advances in generative protein design have enabled de novo antibody generation with explicit target and epitope conditioning. However, most existing approaches remain formulated around a single antigen-antibody interface, whereas bispecific antibody design requires modeling multi-component complexes in which multiple target-recognition interfaces must coexist and interact within a shared antibody structure. Here we present OFAntibody, an all-atom generative framework for de novo design of monoclonal and bispecific antibodies. OFAntibody expands CDR-epitope interaction learning with large-scale distilled antigen-antibody complexes, and introduces multi-component structural supervision and arm-aware multi-hotspot routing to learn compatible multi-interface geometries and couple each antibody paratope to its designated epitope. OFAntibody supports epitope-conditioned generation across monoclonal antibodies and diverse bispecific formats, including tandem VHH, diabody and CODV. In nanobody design benchmarks, OFAntibody achieves a Top-5 enrichment rate of 41.5%, representing a 5.39-fold improvement over RFantibody in competitive candidate ranking. In bispecific antibody design tasks, OFAntibody achieves hotspot pass rates of 94-100% across the three evaluated tasks and achieves energy pass rates of 60%, 13% and 8% for diabody, tandem VHH and CODV formats, respectively. OFAntibody further enables the same target combination to be explored across different antibody formats, while joint multi-interface generation reduces geometric incompatibilities arising from independent design and post hoc assembly. Together, these results extend de novo antibody design from binary antigen-antibody complexes to programmable multi-component complexes, providing a foundation for designing single molecules that combine recognition of distinct targets and their associated biological functions.
