SSPPI: Cross-Modality Enhanced Protein-Protein Interaction Prediction From Sequence and Structure Perspectives.

Abstract

Recent advances have shown great promise in mining multimodal protein knowledge for better protein-protein interaction (PPI) prediction by enriching the representation of proteins. However, existing solutions lack a comprehensive consideration of both local patterns and global dependencies in proteins, hindering the full exploitation of modal information. Additionally, the inherent disparities between modalities are often disregarded, which may lead to inferior modality complementarity effects. To address these issues, we propose a cross-modality enhanced PPI prediction method from the perspectives of protein sequence and structure modalities, namely SSPPI. In this framework, our main contribution is that we integrate both sequence and structural modalities of proteins and employ an alignment and fusion method between modalities to further generate more comprehensive protein representations for PPI prediction. Specifically, we design two modal representation modules (Convformer and Graphormer) tailored for protein sequence and structure modalities, respectively, to enhance the quality of modal representation. Subsequently, we introduce a Cross-modality enhancer module to achieve alignment and fusion between modalities, thereby generating more informative modal joint representations. Finally, we devise a cross-protein fusion (CPF) module to model residue interaction processes between proteins, thereby enriching the joint representation of protein pairs. Extensive experimentation on four benchmark datasets demonstrates that our proposed model surpasses all current state-of-the-art (SOTA) methods. The source codes are publicly available at the following link https://github.com/bixiangpeng/SSPPI/.