<scp>DiffPepDock</scp> : Efficient protein–peptide docking and binder screening via <scp>SE</scp> (3)‐equivariant diffusion

Accurate modeling of protein-peptide interactions is critical for elucidating peptide-mediated biological processes and advancing drug discovery. While traditional methods and recent deep learning-based approaches have shown promise, they often face limitations in accuracy, generalizability, computational efficiency, and the integration of prior binding knowledge. Here, we present DiffPepDock, an efficient protein-peptide docking tool based on SE(3)-equivariant diffusion models. DiffPepDock is pretrained on a carefully curated synthetic dataset of protein-fragment complexes and subsequently fine-tuned on high-quality experimental protein-peptide structures, combining generalization capability with task-specific accuracy. It also supports incorporation of user-specified binding priors including known binding motifs or reference ligands to facilitate pocket selection and enhance docking accuracy. Benchmarking on a non-redundant time-split test set demonstrates that DiffPepDock achieves accuracy comparable to state-of-the-art methods such as AlphaFold3, while substantially reducing inference time. Case studies further underscore its capability in accurately reconstructing native binding structures, particularly in scenarios where AlphaFold3 exhibits limitations. Moreover, DiffPepDock shows competitive in silico screening performance for identifying true peptide binders on AlphaFold-predicted targets, underscoring its practical utility in real-world applications. We anticipate that DiffPepDock offers a practical and reliable tool for protein-peptide docking, complementing existing biomolecular structure prediction methods and contributing to peptide therapeutic modeling efforts. The DiffPepDock tool is publicly available at https://github.com/YuzheWangPKU/DiffPepBuilder, with an interactive demonstration provided via Google Colab at https://colab.research.google.com/github/YuzheWangPKU/DiffPepBuilder/blob/main/examples/DiffPepDock_demo.ipynb.

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Publication Details

Journal
Protein Science
Published
2025-10-14
DOI
https://doi.org/10.1002/pro.70338
Citations
2
Primary Topic
Monoclonal and Polyclonal Antibodies Research
Type
article
Field-Weighted Citation Impact
2.72

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article

DiffPepDock : Efficient protein–peptide docking and binder screening via SE (3)‐equivariant diffusion

Y.X. Wang, Luhua Lai, Fanhao Wang, Laiyi Feng et al.
2 citations
Protein Science
Monoclonal and Polyclonal Antibodies Research
2.72
article

DiffPepDock : Efficient protein–peptide docking and binder screening via SE (3)‐equivariant diffusion

Y.X. Wang, Luhua Lai, Fanhao Wang, Laiyi Feng, Changsheng Zhang
article en
2 citations

Abstract

Accurate modeling of protein-peptide interactions is critical for elucidating peptide-mediated biological processes and advancing drug discovery. While traditional methods and recent deep learning-based approaches have shown promise, they often face limitations in accuracy, generalizability, computational efficiency, and the integration of prior binding knowledge. Here, we present DiffPepDock, an efficient protein-peptide docking tool based on SE(3)-equivariant diffusion models. DiffPepDock is pretrained on a carefully curated synthetic dataset of protein-fragment complexes and subsequently fine-tuned on high-quality experimental protein-peptide structures, combining generalization capability with task-specific accuracy. It also supports incorporation of user-specified binding priors including known binding motifs or reference ligands to facilitate pocket selection and enhance docking accuracy. Benchmarking on a non-redundant time-split test set demonstrates that DiffPepDock achieves accuracy comparable to state-of-the-art methods such as AlphaFold3, while substantially reducing inference time. Case studies further underscore its capability in accurately reconstructing native binding structures, particularly in scenarios where AlphaFold3 exhibits limitations. Moreover, DiffPepDock shows competitive in silico screening performance for identifying true peptide binders on AlphaFold-predicted targets, underscoring its practical utility in real-world applications. We anticipate that DiffPepDock offers a practical and reliable tool for protein-peptide docking, complementing existing biomolecular structure prediction methods and contributing to peptide therapeutic modeling efforts. The DiffPepDock tool is publicly available at https://github.com/YuzheWangPKU/DiffPepBuilder, with an interactive demonstration provided via Google Colab at https://colab.research.google.com/github/YuzheWangPKU/DiffPepBuilder/blob/main/examples/DiffPepDock_demo.ipynb.

Protein ScienceVol. 34(11)
Peking University (CN), Beijing National Laboratory for Molecular Sciences (CN), Center for Life Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Medical Sciences, National Key Research and Development Program of China
Openalex Percentile: Top 8%
Monoclonal and Polyclonal Antibodies Research
2.72
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