WUREN: Whole-modal fUsion Representation for protein interaction interfacE predictioN

Abstract

Proteins are one of the most important components in life, and the research on protein complex and the development of protein or antibody drugs relies on effective representation of proteins. Both experimental methods like cryo-electron microscopy and computational methods like molecular dynamic simulation suffer from high cost, long time investment and low throughput, and cannot be used in large-scale studies. Some examples of artificial intelligence for protein complex prediction tasks show that different representations of proteins have their own limitations. This paper constructs a multimodal model named WUREN (Whole-modal fUsion Representation for protein interaction interfacE predictioN), which effectively fuses sequence, graph, and structural features. WUREN has achieved state-of-the-art performance on both the antigen epitope prediction task and the protein-protein interaction interface prediction task, with AUC-PR reaching 0.462 and 0.516, respectively. Our results show that WUREN is a general and effective feature extraction model for protein complex, which can be used in the development of protein-based drugs. Furthermore, the general framework in WUREN can be potentially applied to model similar biologics to proteins, such as DNA and RNA.