PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915569909891072 |
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| author | Zheng, Xinzhe Du, Hao Xu, Fanding Li, Jinzhe Liu, Zhiyuan Wang, Wenkang Chen, Tao Ouyang, Wanli Li, Stan Z. Lu, Yan Dong, Nanqing Zhang, Yang |
| author_facet | Zheng, Xinzhe Du, Hao Xu, Fanding Li, Jinzhe Liu, Zhiyuan Wang, Wenkang Chen, Tao Ouyang, Wanli Li, Stan Z. Lu, Yan Dong, Nanqing Zhang, Yang |
| contents | Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biology research. To address this gap, we introduce PRING, the first comprehensive benchmark that evaluates protein-protein interaction prediction from a graph-level perspective. PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions, with well-designed strategies to address both data redundancy and leakage. Building on this golden-standard dataset, we establish two complementary evaluation paradigms: (1) topology-oriented tasks, which assess intra and cross-species PPI network construction, and (2) function-oriented tasks, including protein complex pathway prediction, GO module analysis, and essential protein justification. These evaluations not only reflect the model's capability to understand the network topology but also facilitate protein function annotation, biological module detection, and even disease mechanism analysis. Extensive experiments on four representative model categories, consisting of sequence similarity-based, naive sequence-based, protein language model-based, and structure-based approaches, demonstrate that current PPI models have potential limitations in recovering both structural and functional properties of PPI networks, highlighting the gap in supporting real-world biological applications. We believe PRING provides a reliable platform to guide the development of more effective PPI prediction models for the community. The dataset and source code of PRING are available at https://github.com/SophieSarceau/PRING. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs Zheng, Xinzhe Du, Hao Xu, Fanding Li, Jinzhe Liu, Zhiyuan Wang, Wenkang Chen, Tao Ouyang, Wanli Li, Stan Z. Lu, Yan Dong, Nanqing Zhang, Yang Machine Learning Artificial Intelligence Biomolecules Molecular Networks Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biology research. To address this gap, we introduce PRING, the first comprehensive benchmark that evaluates protein-protein interaction prediction from a graph-level perspective. PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions, with well-designed strategies to address both data redundancy and leakage. Building on this golden-standard dataset, we establish two complementary evaluation paradigms: (1) topology-oriented tasks, which assess intra and cross-species PPI network construction, and (2) function-oriented tasks, including protein complex pathway prediction, GO module analysis, and essential protein justification. These evaluations not only reflect the model's capability to understand the network topology but also facilitate protein function annotation, biological module detection, and even disease mechanism analysis. Extensive experiments on four representative model categories, consisting of sequence similarity-based, naive sequence-based, protein language model-based, and structure-based approaches, demonstrate that current PPI models have potential limitations in recovering both structural and functional properties of PPI networks, highlighting the gap in supporting real-world biological applications. We believe PRING provides a reliable platform to guide the development of more effective PPI prediction models for the community. The dataset and source code of PRING are available at https://github.com/SophieSarceau/PRING. |
| title | PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs |
| topic | Machine Learning Artificial Intelligence Biomolecules Molecular Networks |
| url | https://arxiv.org/abs/2507.05101 |