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<p>Therapeutic peptides represent the forefront of drug discovery, offering potent and safe alternatives to traditional small molecules. However, their weak and context-dependent nature complicates the efficient virtual screening and structural characterization of protein–peptide patterns. Here we introduce RAPiDock, a diffusion generative model designed for rational, accurate and rapid protein–peptide docking at an all-atomic level. RAPiDock efficiently reduces the sampling space by incorporating physical constraints and uses a bi-scale graph to effectively capture multidimensional structural information while balancing efficiency. In addition, the model uses a Clebsch–Gordan tensor product-based architecture to ensure physical symmetry. RAPiDock outperforms existing tools in prediction of protein–peptide-binding patterns, achieving a 93.7% success rate at top-25 predictions (13.4% higher than AlphaFold2-Multimer), with an execution speed of 0.35 seconds per complex (~270 times faster than AlphaFold2-Multimer). Extensive experiments demonstrate RAPiDock’s remarkable ability to handle 92 types of residue including posttranslational modifications, accurately predict subtle docking patterns, successfully identify multiple potential peptide-binding sites in global docking and serve as a powerful tool for high-throughput virtual screening with structural precision. All these push the boundaries of efficient protein–peptide docking in multiple real-application scenarios.</p>

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Protein–peptide docking with a rational and accurate diffusion generative model

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • Huifeng Zhao,
  • Odin Zhang,
  • Dejun Jiang,
  • Zhenxing Wu,
  • Hongyan Du,
  • Xiaorui Wang,
  • Yihao Zhao,
  • Yuansheng Huang,
  • Jingxuan Ge,
  • Tingjun Hou,
  • Yu Kang

摘要

Therapeutic peptides represent the forefront of drug discovery, offering potent and safe alternatives to traditional small molecules. However, their weak and context-dependent nature complicates the efficient virtual screening and structural characterization of protein–peptide patterns. Here we introduce RAPiDock, a diffusion generative model designed for rational, accurate and rapid protein–peptide docking at an all-atomic level. RAPiDock efficiently reduces the sampling space by incorporating physical constraints and uses a bi-scale graph to effectively capture multidimensional structural information while balancing efficiency. In addition, the model uses a Clebsch–Gordan tensor product-based architecture to ensure physical symmetry. RAPiDock outperforms existing tools in prediction of protein–peptide-binding patterns, achieving a 93.7% success rate at top-25 predictions (13.4% higher than AlphaFold2-Multimer), with an execution speed of 0.35 seconds per complex (~270 times faster than AlphaFold2-Multimer). Extensive experiments demonstrate RAPiDock’s remarkable ability to handle 92 types of residue including posttranslational modifications, accurately predict subtle docking patterns, successfully identify multiple potential peptide-binding sites in global docking and serve as a powerful tool for high-throughput virtual screening with structural precision. All these push the boundaries of efficient protein–peptide docking in multiple real-application scenarios.