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An interaction-derived graph learning framework for scoring protein–peptide complexes
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AI
DOI：10.1038/s42256-025-01136-1.png)
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Abstract
En 中文
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. GraphPep presents an interaction-derived and protein language model-powered graph learning framework for robust scoring of protein–peptide complexes, substantially enhancing the binding mode prediction of protein–peptide docking.
Keywords:
GraphPep
protein–peptide interactions
graph neural network
scoring function
ESM-2
Journal
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23.9
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1.3K
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