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<p>Intrinsically disordered proteins and regions (IDRs) lack stable 3D structures, posing challenges for interaction prediction. We present SpatPPI, a geometric deep learning model tailored for IDPPI prediction. SpatPPI leverages structural cues from folded domains to guide the dynamic adjustment of IDRs via geometric modeling, adaptive conformation refinement, and a two-stage decoding mechanism. It captures spatial variability without requiring supervised input and achieves state-of-the-art performance on benchmark datasets. Molecular dynamics simulations further validate its high adaptability to conformational changes in IDRs and strong capacity to generate distinct and structure-aware embeddings. A freely accessible server is available at <a href="http://liulab.top/SpatPPI/server">http://liulab.top/SpatPPI/server</a>.</p>

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SpatPPI: a geometric deep learning model for predicting protein–protein interactions involving intrinsically disordered regions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Zeyu Xu,
  • Yanhao Zhu,
  • Jiyun Han,
  • Juntao Liu

摘要

Intrinsically disordered proteins and regions (IDRs) lack stable 3D structures, posing challenges for interaction prediction. We present SpatPPI, a geometric deep learning model tailored for IDPPI prediction. SpatPPI leverages structural cues from folded domains to guide the dynamic adjustment of IDRs via geometric modeling, adaptive conformation refinement, and a two-stage decoding mechanism. It captures spatial variability without requiring supervised input and achieves state-of-the-art performance on benchmark datasets. Molecular dynamics simulations further validate its high adaptability to conformational changes in IDRs and strong capacity to generate distinct and structure-aware embeddings. A freely accessible server is available at http://liulab.top/SpatPPI/server.