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Graph-based deep learning approach for high-throughput protein-DNA interaction scoring

Acta Pharmacologica Sinica volume 47, pages 977–989 (2026)Cite this article

Abstract

Accurately quantifying protein-DNA interactions (PDIs) is critical for understanding biological processes and facilitating drug design. However, the inherent flexibility of nucleic acids limits the availability of experimentally determined structures of PDI complexes, posing a significant challenge for training reliable scoring functions (SFs). To address this, we developed PDIScore, a novel deep learning-based SF for PDI prediction. PDIScore utilizes a comprehensive graph representation to capture nucleotide flexibility, employs a scalable GraphGPS architecture with BigBird linear global attention to handle large interaction interfaces, and leverages Mixture Density Networks (MDNs) to model residue-nucleotide distance distributions. PDIScore was trained on a self-collected dataset of ~7000 protein-nucleic acid complex structures and validated on three rigorous test sets for evaluating its screening, docking, and ranking capabilities. The results illustrated that PDIScore significantly outperformed existing methods: it achieved the best screening power on the screening set (e.g., EF1% = 14.13, AUROC = 0.82 using AlphaFold3 structures), the highest docking success rate on the docking set (48.94% top1), and superior ranking capability on the ranking set (PCC = 0.50). Case studies demonstrated PDIScore’s ability to elucidate biological mechanisms (e.g., adenovirus transcription, SOCS1 regulation) and its interpretability at the nucleotide level for identifying key interaction sites. PDIScore represents a robust, generalizable tool with significant potential for advancing PDI-related research and therapeutic design.

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Fig. 1: The model architecture of PDIScore.
Fig. 2: Screening and ranking powers of SFs on the screening set.
Fig. 3: Docking powers of SFs on the docking benchmark with two cases.
Fig. 4: Ranking powers of SFs on the ranking dataset.
Fig. 5: PDIScore predicts the transcriptional activity of the promoter in the regulation of SOCS1 by Egr1.
Fig. 6: The decomposed contributions of PDIScore at the nucleotide level.

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Data availability

The datasets are available at https://doi.org/10.5281/zenodo.13764615. The code is available at https://github.com/roger-yh-zhao/pdiscore.

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (22220102001, 22307112, 92370130, 22303081) and Postdoctoral Fellowship Program of CPSF (GZB20230648).

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Author notes

  1. These authors contributed equally: Yi-hao Zhao, Ying Wang.

Authors and Affiliations

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

    Yi-hao Zhao, Ying Wang, Shu-kai Gu, Hui-feng Zhao, Zi-yi You, Ting-jun Hou & Yu Kang

  2. Department of Clinical Pharmacy, the First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310003, China

    Chao Shen

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

    De-jun Jiang

  4. Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua, 321016, China

    Ting-jun Hou & Yu Kang

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Contributions

TJH and YK designed the research study. YHZ developed the method and wrote the code. YHZ, YW, CS, DJJ, SKG, HFZ, and ZYY performed the analysis. YHZ and TJH wrote the paper. All authors read and approved the manuscript.

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Correspondence to Ting-jun Hou or Yu Kang.

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Zhao, Yh., Wang, Y., Shen, C. et al. Graph-based deep learning approach for high-throughput protein-DNA interaction scoring. Acta Pharmacol Sin 47, 977–989 (2026). https://doi.org/10.1038/s41401-025-01688-3

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