Identifying virus-receptor interactions through matrix completion with similarity fusion

Viral infectious diseases have become a serious threat to human health and global security. The receptor-binding is the first step of viral infection. Identifying potential virus-receptor interactions gives us a new perspective on understanding interaction mechanisms, and further find potential targets for prevention and therapy of viral infectious diseases. Many predicted methods have been proposed to identify potential virus-receptor interactions. They didn’t focus on fusing multiple biological features and using multiple similarity measures depending on multiple biological characteristics to improve the prediction performance. In this study, a novel predictive method (LOMCVRI) is proposed to identify potential VirusReceptor Interactions based on similarity fusion and Matrix Completion of Linear optimization. In LOMCVRI, the viral protein structure similarity, the viral protein sequence similarity and the viral genomic sequence similarity are computed based on viral secondary structure features, viral protein sequences and viral genomic sequences, respectively. The gaussian radial basis function is used to improve these viral similarities. They are further combined into a compositive viral similarity by the linear weighting average method. Second, we compute the receptor conserved domain sequence similarity, the receptor sequence similarity and the receptor protein-protein interaction network (PPI) similarity based on the receptor conserved domain sequences, the receptor amino acid sequences and the human PPI data. They are also fused into a compositive receptor similarity. Next, the K-nearest neighbor preprocessing is employed to prefill missing entries in original interaction matrix. Finally, based on updated virus-receptor interactions and compositive similarities of virus and receptor, the matrix completion of linear optimization is applied to identify potential virus-receptor interactions. 10-fold Cross-Validation (10CV) and Leave-One-Out Cross-Validation (LOOCV) experimental results show that the area under of ROC curve (AUC) values of LOMCVRI are 0.9231 and 0.9438, respectively, which consistently outperforms all other related methods. In addition, a case study further confirms the effectiveness of our method.