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Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions | Acta Pharmacologica Sinica
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Saved: 2026-01-11T04:00:37.840Z
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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Additional access options: Log in Learn about institutional subscriptions Read our FAQs Contact customer support Similar content being viewed by others Learning functional properties of proteins with language models Article 21 March 2022 Single-sequence protein structure prediction using a language model and deep learning Article 03 October 2022 Evaluating protein binding interfaces with transformer networks Article 07 September 2023 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).
Author information 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