Effective Protein-Protein Interaction Exploration with PPIretrieval
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913224605040640 |
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| author | Hua, Chenqing Coley, Connor Wolf, Guy Precup, Doina Zheng, Shuangjia |
| author_facet | Hua, Chenqing Coley, Connor Wolf, Guy Precup, Doina Zheng, Shuangjia |
| contents | Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effective Protein-Protein Interaction Exploration with PPIretrieval Hua, Chenqing Coley, Connor Wolf, Guy Precup, Doina Zheng, Shuangjia Biomolecules Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes. |
| title | Effective Protein-Protein Interaction Exploration with PPIretrieval |
| topic | Biomolecules Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2402.03675 |