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<p>AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody interactions, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in average top-10 precision (Precision<i>@</i>10) for CASP 13 and 14 targets and 31.94% for DIPS-Plus datasets on high-quality structures. While performance naturally declines on the more challenging unbound complexes due to large conformational changes, ProTact maintains a competitive edge over baselines. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design.</p>

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Accurate protein-protein interactions modeling through physics-informed geometric invariant learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Jiahua Rao,
  • Deqin Liu,
  • Xiaolong Zhou,
  • Qianmu Yuan,
  • Wentao Wei,
  • Wei Lu,
  • Jixian Zhang,
  • Yu Rong,
  • Yuedong Yang,
  • Shuangjia Zheng

摘要

AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody interactions, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in average top-10 precision (Precision@10) for CASP 13 and 14 targets and 31.94% for DIPS-Plus datasets on high-quality structures. While performance naturally declines on the more challenging unbound complexes due to large conformational changes, ProTact maintains a competitive edge over baselines. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design.