Graph neural network integrated with pretrained protein language model for predicting human-virus protein-protein interactions.

ABSTRACT: The systematic identification of human-virus protein-protein interactions (PPIs) is a critical step toward elucidating the underlying mechanisms of viral infection, directly informing the development of targeted interventions against existing and emerging viral threats. In this work, we presented DeepGNHV, an end-to-end framework that integrated a pretrained protein language model with structural features derived from AlphaFold2 and leveraged graph attention networks to predict human-virus PPIs. In comparison to other state-of-the-art approaches, DeepGNHV exhibited superior predictive performance, especially when applied to viral proteins absent from the training process, indicating its strong generalization capability for detecting newly emerging virus-related PPIs. We further demonstrate

SUBMITTER: Jiang L 

PROVIDER: S-EPMC12415850 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature