DMVHP-IBS: Dynamic feature-integrated multi-modal prediction of virus-host protein interactions and the binding sites.

Abstract

Accurately predicting virus-host protein interactions(VH-PPI) and their binding sites is essential for understanding viral pathogenic mechanisms and developing drugs and vaccines. Existing sequence- or structure-based approaches still have limitations in extracting dynamic information and identifying binding sites, which constrains their practical applications. In this study, we propose a multimodal data-driven graph convolutional neural network model named DMVHP-IBS, which integrates dynamic and static protein information. Through the fusion of protein dynamic and structural attributes into graph data, alongside the encoding and feature extraction from protein sequences, the model assembles multimodal data for the prediction of viral-host protein interactions and binding sites. To further elucidate the binding mechanisms between viral-host proteins, we introduce a biologically inspired binding site prediction method called Gradient-Enhanced Interaction Contribution Analysis (GEICA) which can highlight key binding residues. The results show that DMVHP-IBS outperforms state-of-the-art methods across various viral datasets, demonstrating its generalizability and robustness. In the binding site prediction, we successfully identified key binding sites between VH-PPI, and between proteins and drugs by leveraging the self-attention mechanisms of GEICA and ProtBERT. DMVHP-IBS is useful in the design of drugs, targeted therapeutics, and antibodies.