ECA-PHV: Predicting human-virus protein-protein interactions through an interpretable model of effective channel attention mechanism

• Published in: Chemometrics and intelligent laboratory systems 2024-04, Vol.247, p.105103, Article 105103
• Main Authors: Wang, Minghui, Lai, Jiali, Jia, Jihua, Xu, Fei, Zhou, Hongyan, Yu, Bin
• Format: Article
• Language: English
• Subjects: Deep learning; Effective channel attention; Group Lasso; Human-virus protein-protein interaction; Multi-information fusion
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2024.105103

The prediction of human-virus protein-protein interactions (human-virus PPIs) is significant for exploring the mechanisms of viral infection, making their prediction a necessary and practically valuable research topic. Since conventional methods for the determination of human-virus protein-protein interactions are very complex and expensive, the construction of models plays a crucial role. In this paper, we construct an interpretable model, ECA-PHV, to predict human-virus protein-protein interactions based on an effective channel attention mechanism. First, we utilize five coding modalities, namely AAC, DDE, MMI, CT, and GTPC, to extract the hidden biological information in protein sequences. Individual feature weights are then learned by using a differential evolutionary algorithm that employs weighted combinations to adequately represent various protein sequence information. Next, irrelevant features in multi-information fusion are removed by Group Lasso. Finally, the prediction model is constructed by combining effective channel attention, BiGRU, and 1D-CNN. Compared with existing models, the interpretability framework ECA-PHV proposed in this paper has competitive and stable predictive performance. This shows that our model can efficiently focus on important information about protein sequences. In conclusion, this study accelerates the exploration of human-virus protein-protein interactions and provides some insights of practical value for probing human-virus relationships. •A novel method (ECA-PHV) to predict human-virus protein-protein interactions (human-virus PPIs).•The AAC, DDE, CT, MMI, and GTPC methods are fused to extract protein sequence feature information.•We first constructed an interpretable deep learning framework that combines BiGRU, 1D-CNN, and efficient channel attention to predict human-virus protein-protein interactions.•ECA-PHV increases the prediction performance compared with other methods.