Full-Atom Protein-Protein Interaction Prediction via Atomic Equivariant Attention Network

Le résumé fourni par la source

Protein-protein Interaction (PPI) prediction, which aims to identify the interactions between proteins within a biological system, is an important problem in understanding disease mechanisms and drug discovery. Recently, Equivariant Graph Neural Networks (E3-GNNs) are advanced computational models that provide a powerful solution for accurately predicting PPIs by preserving the geometric integrity of protein interactions. However, most E3-GNNs model protein interactions at the residue level, potentially neglecting critical atomic details and side-chain conformations. In this paper, we propose a novel model, MEANT, designed to adaptively extract atom-level geometric information from varying numbers of atoms within different residues for PPI prediction. Specifically, we define a full-atom graph that contains atomic geometry and guides the message passing under the structure of residues. We also design a geometric relation extractor to integrate geometric information from different residues and adaptively handle variations in the number of atoms within each residue. Finally, we adopt the attention mechanism to update the residue representation and the atomic coordinates within a residue. Experimental results show that our proposed model, MEANT, significantly outperforms state-of-the-art methods on three typical PPI prediction tasks. Our code and data are available on GitHub at https://github.com/BUPT-GAMMA/MEANT.

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