Effective Protein-Protein Interaction Exploration with PPIretrieval
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 47 references that are in the map. First posted on arXiv 6 Feb 2024.
Builds on
16 of 47 references.
References · 16
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4 more
- 2022Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networkscited by 19
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Citing papers per year: 2024: 5.
5 papers.
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- 2024The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges12 Jul 2024