Abstract
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.
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Data availability
The processed datasets, as well as pretrained models, are available via Zenodo at https://doi.org/10.5281/zenodo.14193621 (ref. 77). Unbound protein–peptide complexes were taken from the PepSet benchmark package (http://cadd.zju.edu.cn/pepset/statics/binaryDownload/benchmark.tar.gz). Source data are provided with this paper.
Code availability
The source code of this study is freely available via GitHub at https://github.com/huifengzhao/RAPiDock and Zenodo at https://doi.org/10.5281/zenodo.14568726 (ref. 78).
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Acknowledgements
This study was financially supported by the National Key R&D Program of China (grant number 2024YFA1307501 to T.H.), National Natural Science Foundation of China (grant number 82373791 to Y.K.), National Natural Science Foundation of China (grant number 22220102001 to T.H.), National Natural Science Foundation of China (grant number 22307112 to D.J.) and the Young Scientists Fund of Natural Science Foundation of Hunan Province of China (grant number 2025JJ60651 to D.J.). We thank the Information Technology Center and State Key Laboratory of CAD&CG, Zhejiang University.
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Y.K., T.H., H.Z. and O.Z. designed the research study. H.Z. developed the method and wrote the code. H.Z., O.Z., D.J., Z.W., H.D., X.W., Y.Z., Y.H. and J.G. performed the analysis. H.Z., Y.K., T.H. and O.Z. wrote the paper. All authors read and approved the paper.
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Extended data
Extended Data Fig. 1 Effects of various factors on the docking performance of RAPiDock in the RefPepDB-RecentSet (compared to AF2Multi_RS).
(a) shows the effect of peptide lengths. (b) shows the effect of ASA%. (c) shows the effect of different second structures. Secondary structure categorization follows DSSP standards: ‘Loop’ includes Turn, Bend, and Coil; ‘Helix’ encompasses 310 helix, π helix, and α helix; ‘PPII’ is defined as polyproline II helix; ‘Strand’ covers beta bridge and strand structures. The box plots show the 25–75% confidence intervals (box limits), the median (centre line), and the 5–95% confidence intervals (whiskers). Green triangles indicate the mean values. N values report the number of clusters in each band.
Extended Data Fig. 2 Superior local and global docking performance of RAPiDock on more challenging tasks.
(a-c) illustrate the Top N success rates of various methods based on the CAPRI-peptide standards. Solid lines represent local docking approaches, while dashed lines represent global docking ones, (d-f) show the Top1, Top5 and Top25 success rates of various methods. The bars above the dashed line represent the performance of local docking approaches, while the bars below show that of global docking approaches. According to CAPRI-peptide standards, three accuracy levels are considered, namely Acceptable Quality (a and d), Medium Quality (b and e), and High Quality (c, f).
Extended Data Fig. 3 The docking performance of RAPiDock on SHP2.
(a) The sequences of 5X7B (N-SH2 domain of SHP2) and 5X94 (C-SH2 domain of SHP2). (b) left: The overlay of protein residues of 5X7B and 5X94. In SH2 domains, two structural regions determine the PpI: (1) the phosphotyrosine binding site, a groove-like structure formed by the αA helix, βB/βC/βD strands, and BC loop, and (2) the specificity pocket, which is a relatively large hydrophobic pocket mainly delimited by residues of αB helix, βD strand, and BG and EF loops. The phosphotyrosine binding site is relatively conserved, whereas the specificity pocket is more flexible; right: the peptides of 5X7B and 5X94 share a common 7-residue sequence PIpYATID. Given the pTyr residue serving as a reference, the residues from −2 to +3 align well in the pocket. The residues at +4 of the peptides exhibit obvious deviation, influenced by structural differences in the protein near the BG loop. Specifically, the proteins of 5X7B and 5X94 are colored green and yellow, respectively, while their peptides are colored white and gray, respectively. (c) Top-ranked peptide conformation generated by RAPiDock for 5X7B. (d) Top- ranked peptide conformation generated by RAPiDock for 5X94. The peptide conformations generated by RAPiDock are colored coral red.
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Supplementary Notes 1–8, Figs. 1–37 and Tables 1–10.
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Zhao, H., Zhang, O., Jiang, D. et al. Protein–peptide docking with a rational and accurate diffusion generative model. Nat Mach Intell 7, 1308–1321 (2025). https://doi.org/10.1038/s42256-025-01077-9
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DOI: https://doi.org/10.1038/s42256-025-01077-9