  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.
