Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929653605728256 |
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| author | Wu, Lirong Lin, Haitao Huang, Yufei Gao, Zhangyang Tan, Cheng Liu, Yunfan Wu, Tailin Li, Stan Z. |
| author_facet | Wu, Lirong Lin, Haitao Huang, Yufei Gao, Zhangyang Tan, Cheng Liu, Yunfan Wu, Tailin Li, Stan Z. |
| contents | Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody. Despite the great progress made in CDR design, existing computational methods still encounter several challenges: 1) poor capability of modeling complex CDRs with long sequences due to insufficient contextual information; 2) conditioned on pre-given antigenic epitopes and their static interaction with the target antibody; 3) neglect of specificity during antibody optimization leads to non-specific antibodies. In this paper, we take into account a variety of node features, edge features, and edge relations to include more contextual and geometric information. We propose a novel Relation-Aware Antibody Design (RAAD) framework, which dynamically models antigen-antibody interactions for co-designing the sequences and structures of antigen-specific CDRs. Furthermore, we propose a new evaluation metric to better measure antibody specificity and develop a contrasting specificity-enhancing constraint to optimize the specificity of antibodies. Extensive experiments have demonstrated the superior capability of RAAD in terms of antibody modeling, generation, and optimization across different CDR types, sequence lengths, pre-training strategies, and input contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization Wu, Lirong Lin, Haitao Huang, Yufei Gao, Zhangyang Tan, Cheng Liu, Yunfan Wu, Tailin Li, Stan Z. Quantitative Methods Artificial Intelligence Machine Learning Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody. Despite the great progress made in CDR design, existing computational methods still encounter several challenges: 1) poor capability of modeling complex CDRs with long sequences due to insufficient contextual information; 2) conditioned on pre-given antigenic epitopes and their static interaction with the target antibody; 3) neglect of specificity during antibody optimization leads to non-specific antibodies. In this paper, we take into account a variety of node features, edge features, and edge relations to include more contextual and geometric information. We propose a novel Relation-Aware Antibody Design (RAAD) framework, which dynamically models antigen-antibody interactions for co-designing the sequences and structures of antigen-specific CDRs. Furthermore, we propose a new evaluation metric to better measure antibody specificity and develop a contrasting specificity-enhancing constraint to optimize the specificity of antibodies. Extensive experiments have demonstrated the superior capability of RAAD in terms of antibody modeling, generation, and optimization across different CDR types, sequence lengths, pre-training strategies, and input contexts. |
| title | Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.00013 |