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# Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions | Acta Pharmacologica Sinica

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Article
Published: 01 October 2025
Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions
De-jun Jiang, Hui-feng Zhao, Hong-yan Du, Yu Kang, Pei-chen Pan, Zhen-xing Wu, Yun-dian Zeng, O-din Zhang, Xiao-rui Wang, Ji-ke Wang, Yuan-sheng Huang, Yi-hao Zhao, Chang-Yu Hsieh, Dong-sheng Cao, Hui-yong Sun & Ting-jun Hou

Acta Pharmacologica Sinica (2025)Cite this article

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Abstract

Protein-peptide interactions (PpIs) play a critical role in major cellular processes. Recently, a number of machine learning (ML)-based methods have been developed to predict PpIs, but most of them rely heavily on sequence data, limiting their ability to capture the generalized molecular interactions in three-dimensional (3D) space, which is crucial for understanding protein-peptide binding mechanisms and advancing peptide therapeutics. Protein-peptide docking approaches provide a feasible way to generate the 3D models of PpIs, but they often suffer from low-precision scoring functions (SFs). To address this, we developed DeepPpIScore, a novel SF for PpIs that employs unsupervised geometric deep learning coupled with a physics-inspired statistical potential. Trained solely on curated experimental structures without binding affinity data or classification labels, DeepPpIScore exhibits broad generalization across multiple tasks. Our comprehensive evaluations in bound and unbound peptide bioactive conformation prediction, binding affinity prediction, and binding pair identification reveal that DeepPpIScore outperforms or matches state-of-the-art baselines, including popular protein-protein SFs, ML-based methods, and AlphaFold-Multimer 2.3 (AF-M 2.3). Notably, DeepPpIScore achieves superior results in peptide binding mode prediction compared to AF-M 2.3. More importantly, DeepPpIScore offers interpretability in terms of hotspot preferences at protein interfaces, physics-informed noncovalent interactions, and protein-peptide binding energies.

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Data availability

The source data and main scripts that implement the computational protocol are available at https://github.com/zjujdj/DeepPpIScore.

Code availability

The source data and main scripts that implement the computational protocol are available at https://github.com/zjujdj/DeepPpIScore.

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (22307112), Young Scientists Fund of Natural Science Foundation of Hunan Province of China (2025JJ60651), the National Key R&D Program of China (2024YFA1307500), Postdoctoral Science Foundation of China (2022M722777), and Postdoctoral Fellowship Program of CPSF (GZB20230648, GZB20230657).

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Author notes

These authors contributed equally: De-jun Jiang, Hui-feng Zhao, Hong-yan Du.

Authors and Affiliations

College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China

De-jun Jiang, Hui-feng Zhao, Hong-yan Du, Yu Kang, Pei-chen Pan, Zhen-xing Wu, O-din Zhang, Xiao-rui Wang, Ji-ke Wang, Yuan-sheng Huang, Yi-hao Zhao, Chang-Yu Hsieh & Ting-jun Hou

Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004, China

De-jun Jiang & Dong-sheng Cao

Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, 310018, China

De-jun Jiang

College of Control Science and Engineering, Zhejiang University, Hangzhou, 310058, China

Yun-dian Zeng

Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, 210009, China

Hui-yong Sun

Contributions

DJJ, HYD, and HFZ contributed to the main code and wrote the manuscript. DJJ performed the experiment. YDZ, ODZ and ZXW provided partial codes of this work. HFZ and XRW helped perform the analysis with constructive discussions. YDZ, JKW, YHZ, and YSH contributed to the visualization and technique support. YK, PCP, HYS, DSC, TJH, and CYH provided essential financial support and conception, and were responsible for the overall quality.

Corresponding authors

Correspondence to Chang-Yu Hsieh, Dong-sheng Cao, Hui-yong Sun or Ting-jun Hou.

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Jiang, Dj., Zhao, Hf., Du, Hy. et al. Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions. Acta Pharmacol Sin (2025). https://doi.org/10.1038/s41401-025-01659-8

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Received
28 June 2025

Accepted
20 August 2025

Published
01 October 2025

Version of record
01 October 2025

DOI
https://doi.org/10.1038/s41401-025-01659-8

Key words
protein-peptide interactions
geometric deep learning
statistical potential
scoring function
AlphaFold

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