dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Tan, Cheng Zhang, Yijie Gao, Zhangyang Huang, Yufei Lin, Haitao Wu, Lirong Wu, Fandi Blanchette, Mathieu Li, Stan. Z. |
| author_facet | Tan, Cheng Zhang, Yijie Gao, Zhangyang Huang, Yufei Lin, Haitao Wu, Lirong Wu, Fandi Blanchette, Mathieu Li, Stan. Z. |
| contents | The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the resulting antibodies. To bridge this gap, we introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures and specifically addresses the dynamic nature of antigen conformation changes. Our dyAb model leverages a unique combination of coarse-grained interface alignment and fine-grained flow matching techniques to simulate the interaction dynamics and structural evolution of the antigen-antibody complex, providing a realistic representation of the binding process. Extensive experiments show that dyAb significantly outperforms existing models in antibody design involving changing antigen conformations. These results highlight dyAb's potential to streamline the design process for therapeutic antibodies, promising more efficient development cycles and improved outcomes in clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01910 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen Tan, Cheng Zhang, Yijie Gao, Zhangyang Huang, Yufei Lin, Haitao Wu, Lirong Wu, Fandi Blanchette, Mathieu Li, Stan. Z. Quantitative Methods Artificial Intelligence The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the resulting antibodies. To bridge this gap, we introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures and specifically addresses the dynamic nature of antigen conformation changes. Our dyAb model leverages a unique combination of coarse-grained interface alignment and fine-grained flow matching techniques to simulate the interaction dynamics and structural evolution of the antigen-antibody complex, providing a realistic representation of the binding process. Extensive experiments show that dyAb significantly outperforms existing models in antibody design involving changing antigen conformations. These results highlight dyAb's potential to streamline the design process for therapeutic antibodies, promising more efficient development cycles and improved outcomes in clinical applications. |
| title | dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01910 |