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An interaction-derived graph learning framework for scoring protein–peptide complexes

Nature Machine Intelligence volume 7, pages 1858–1869 (2025)Cite this article

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Abstract

Accurate prediction of protein–peptide interactions is critical for peptide drug discovery. However, due to the limited number of protein–peptide structures in the Protein Data Bank, it is challenging to train an accurate scoring function for protein–peptide interactions. Here, addressing this challenge, we propose an interaction-derived graph neural network model for scoring protein–peptide complexes, named GraphPep. GraphPep models protein–peptide interactions instead of traditional atoms or residues as graph nodes, and focuses on residue–residue contacts instead of a single peptide root mean square deviation in the loss function. Therefore, GraphPep can not only efficiently capture the most important protein–peptide interactions, but also mitigate the problem of limited training data. Moreover, the power of GraphPep is further enhanced by the ESM-2 protein language model. GraphPep is extensively evaluated on diverse decoy sets generated by various protein–peptide docking programs and AlphaFold, and is compared against state-of-the-art methods. The results demonstrate the accuracy and robustness of GraphPep.

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Fig. 1: The framework of GraphPep.
Fig. 2: Comparison of GraphPep with other methods on the LEADS-PEP bound test set.
Fig. 3: Comparison of GraphPep with other methods on the Local_62 unbound test set.
Fig. 4: Comparison of GraphPep with other methods on the ADCP99 test set.
Fig. 5: Comparison of GraphPep with other methods on the nr_epitope_minus test set.
Fig. 6: Examples of the top-ranked binding modes by HPEPDOCK and GraphPep.

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Data availability

The raw data of the evaluation results are provided in the Article and its Supplementary Information. The protein–peptide structures for testing in this Article were taken from the PDB. The training decoy set and test decoy sets are available via Zenodo at https://doi.org/10.5281/zenodo.17097750 (ref. 68). Source data are provided with this paper.

Code availability

The GraphPep package is freely available for academic or non-commercial users at http://huanglab.phys.hust.edu.cn/GraphPep/or via Zenodo at https://doi.org/10.5281/zenodo.17099863 (ref. 69).

Change history

  • 17 February 2026

    Since the version of the article initially published, the Major Project of Guangzhou National Laboratory grant no. has been corrected to GZNL2023A03007 in the HTML and PDF versions of the article.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (grant nos. 32430020, 32161133002 and 62072199), the Major Project of Guangzhou National Laboratory (GZNL2023A03007) and the startup grant of Huazhong University of Science and Technology.

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Authors and Affiliations

  1. School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, People’s Republic of China

    Huanyu Tao, Xiaoyu Wang & Sheng-You Huang

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  1. Huanyu Tao
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  2. Xiaoyu Wang
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Contributions

S.-Y.H. conceived and supervised the project. H.T. performed the experiments. S.-Y.H. and H.T. analysed the data. H.T. and X.W. tested the program. H.T. and S.-Y.H. wrote the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Sheng-You Huang.

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Nature Machine Intelligence thanks Jianyi Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Tao, H., Wang, X. & Huang, SY. An interaction-derived graph learning framework for scoring protein–peptide complexes. Nat Mach Intell 7, 1858–1869 (2025). https://doi.org/10.1038/s42256-025-01136-1

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