CAME-AB: Cross-Modality Attention with Mixture-of-Experts for Antibody Binding Site Prediction

• URL: http://arxiv.org/abs/2509.06465v4
• Date: Thu, 11 Sep 2025 05:09:47 GMT
• Title: CAME-AB: Cross-Modality Attention with Mixture-of-Experts for Antibody Binding Site Prediction
• Authors: Hongzong Li, Jiahao Ma, Zhanpeng Shi, Rui Xiao, Fanming Jin, Ye-Fan Hu, Hangjun Che, Jian-Dong Huang,
• Abstract summary: bfCAME-AB is a novel Cross-modality Attention framework for antibody binding site prediction.<n>It integrates raw acid encodings, BLOSUM substitution profiles, pretrained language model embeddings, structure-aware features, and biochemical graphs.<n>It consistently outperforms strong baselines on multiple metrics, including Precision, Recall, F1-score, AUC-ROC, and MCC.
• Score: 9.316793780511917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Antibody binding site prediction plays a pivotal role in computational immunology and therapeutic antibody design. Existing sequence or structure methods rely on single-view features and fail to identify antibody-specific binding sites on the antigens. In this paper, we propose \textbf{CAME-AB}, a novel Cross-modality Attention framework with a Mixture-of-Experts (MoE) backbone for robust antibody binding site prediction. CAME-AB integrates five biologically grounded modalities, including raw amino acid encodings, BLOSUM substitution profiles, pretrained language model embeddings, structure-aware features, and GCN-refined biochemical graphs, into a unified multimodal representation. To enhance adaptive cross-modal reasoning, we propose an \emph{adaptive modality fusion} module that learns to dynamically weight each modality based on its global relevance and input-specific contribution. A Transformer encoder combined with an MoE module further promotes feature specialization and capacity expansion. We additionally incorporate a supervised contrastive learning objective to explicitly shape the latent space geometry, encouraging intra-class compactness and inter-class separability. To improve optimization stability and generalization, we apply stochastic weight averaging during training. Extensive experiments on benchmark antibody-antigen datasets demonstrate that CAME-AB consistently outperforms strong baselines on multiple metrics, including Precision, Recall, F1-score, AUC-ROC, and MCC. Ablation studies further validate the effectiveness of each architectural component and the benefit of multimodal feature integration. The model implementation details and the codes are available on https://anonymous.4open.science/r/CAME-AB-C525

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