Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction

• URL: http://arxiv.org/abs/2503.17666v1
• Date: Sat, 22 Mar 2025 06:23:51 GMT
• Title: Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction
• Authors: Peijin Guo, Minghui Li, Hewen Pan, Ruixiang Huang, Lulu Xue, Shengqing Hu, Zikang Guo, Wei Wan, Shengshan Hu,
• Abstract summary: We present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations.<n>Our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance.
• Score: 6.681379194115459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.

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