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Background <p>Accurate identification of protein-protein interaction sites (PPIS) is critical for elucidating biological mechanisms and advancing drug discovery. However, existing methods still face significant challenges in leveraging structural information, including inadequate equivariant modeling, coarse graph representations, and limited multimodal fusion strategies.</p> Results <p>In this study, we propose a novel multimodal and multiscale deep learning framework, EDG-PPIS, that achieves efficient PPIS prediction by jointly enhancing structural and geometric representations. Specifically, a 3D equivariant graph neural network (LEFTNet) is employed to capture the global spatial geometry of proteins. For structural modeling, a dual-scale graph neural network is constructed to extract protein structural features from both local and remote perspectives. Finally, an attention mechanism is utilized to dynamically fuse structural and geometric features, enabling cross-modal integration. Experimental results demonstrate that EDG-PPIS achieves superior performance across multiple benchmark datasets.</p> Conclusions <p>EDG-PPIS provides an effective and robust computational tool for target identification and protein function analysis, addressing existing challenges in PPIS prediction and offering a promising approach for advancing the understanding of PPIS.</p>

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EDG-PPIS: an equivariant and dual-scale graph network for protein–protein interaction site prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Zhixin Zhang,
  • Zhen Li,
  • Wenshuo Li,
  • Qunhao Zhang,
  • Jun Xiao,
  • Shanyang Ding,
  • Yi Han

摘要

Background

Accurate identification of protein-protein interaction sites (PPIS) is critical for elucidating biological mechanisms and advancing drug discovery. However, existing methods still face significant challenges in leveraging structural information, including inadequate equivariant modeling, coarse graph representations, and limited multimodal fusion strategies.

Results

In this study, we propose a novel multimodal and multiscale deep learning framework, EDG-PPIS, that achieves efficient PPIS prediction by jointly enhancing structural and geometric representations. Specifically, a 3D equivariant graph neural network (LEFTNet) is employed to capture the global spatial geometry of proteins. For structural modeling, a dual-scale graph neural network is constructed to extract protein structural features from both local and remote perspectives. Finally, an attention mechanism is utilized to dynamically fuse structural and geometric features, enabling cross-modal integration. Experimental results demonstrate that EDG-PPIS achieves superior performance across multiple benchmark datasets.

Conclusions

EDG-PPIS provides an effective and robust computational tool for target identification and protein function analysis, addressing existing challenges in PPIS prediction and offering a promising approach for advancing the understanding of PPIS.