Effective Protein-Protein Interaction Exploration with PPIretrieval

TL;DR

PPIretrieval is a novel deep learning model that efficiently explores potential protein-protein interactions by embedding protein surface information, aiding in the identification of binding partners and complex formation.

Contribution

It introduces the first deep learning-based approach for PPI exploration using embedding space search, capturing geometric and chemical features of proteins.

Findings

Successfully identifies potential binding partners in unseen proteins.

Leverages rich geometric and chemical information for accurate PPI prediction.

Facilitates formation of protein-protein complexes in embedding space.

Abstract

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.