发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对抗原特异性抗体设计难题，引入AAMFM多模态基础模型，通过跨模态适配器整合抗原信息，用校准直接偏好优化微调，实现抗体 - 抗原相互作用联合建模，实验证明其在功能性抗体设计中性能最优，有抗原特异性抗体工程潜力。
AI 中文摘要
抗体是在免疫识别中通过结合特定抗原分子发挥核心作用的重要蛋白质。尽管近期蛋白质语言模型在单链蛋白质建模和生成方面取得进展，但在抗原特异性抗体设计上常显不足，有效建模需抗体与抗原明确配对，尤其是在表位水平。为解决这些局限，我们引入AAMFM，一种抗原特异性抗体多模态基础模型，它能基于抗原背景学习抗体序列和结构的统一表示。AAMFM通过跨模态适配器整合包括几何界面和表位注释等丰富抗原信息，在共享潜在空间中对抗体 - 抗原相互作用进行联合建模。为进一步引导模型实现功能相关性，我们使用校准直接偏好优化（Cal-DPO）对AAMFM进行微调，利用从强结构先验中提取的偏好信号，使其与结合特异性目标的对齐学习。大量实验表明AAMFM在功能性抗体设计中实现了当前最优性能，揭示了其在抗原特异性抗体工程中的潜力。我们的代码可在这个https网址获取。
英文摘要
Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules. Although recent protein language models have enabled progress in single-chain protein modeling and generation, they often fall short in antigen-specific antibody design, where effective modeling requires explicit pairing between antibody and antigen, particularly at the epitope level. To address these limitations, we introduce AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context. AAMFM incorporates rich antigen information including geometric interfaces and epitope annotations via a cross-modal adapter, enabling joint modeling of antibody-antigen interactions in a shared latent space. To further guide the model toward functional relevance, we fine-tune AAMFM using Calibrated Direct Preference Optimization (Cal-DPO), leveraging preference signals extracted from a strong structural prior to align learning with binding-specific objectives. Extensive experiments demonstrate that AAMFM achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering. Our code is available at https://github.com/XL-S224/AAMFM.
