Identification of Virus-Receptor Interactions Based on Network Enhancement and Similarity.

2020 
As a main composition of the human-associated microbiome, viruses are directly associated with our health and disease. The receptor-binding is critical for the virus infection. So identifying potential virus-receptor interactions will help systematically understand the mechanisms of virus-receptor interactions and effectively treat infectious diseases caused by viruses. Several computational models have been developed to identify virus-receptor interactions based on assumption that similar viruses show similar interaction patterns with receptors and vice versa, but the performance need to be improved. Furthermore, the virus network and the receptor network are also noisy. Therefore, we present a new prediction model (NERLS) to identify potential virus-receptor interactions based on Network Enhancement, virus sequence information and receptor sequence information by Regularized Least Squares. Firstly, the virus network is constructed based on the virus sequence similarity and Gaussian interaction profile (GIP) kernel similarity of viruses by a mean method. They are calculated based on the viral RefSeq genomes downloaded from NCBI and known virus-receptor interactions, respectively. Similarly, we also use the same mean method to construct the receptor network based on the amino acid sequence similarity and known virus-receptor interactions. Then Network Enhancement is applied to denoise the virus network and the receptor network. Finally, we employ the regularized least squares algorithm to identify potential virus-receptor interactions. The 10-fold cross validation (10CV) experimental results indicate that an average Area Under Curve (AUC) values of NERLS is 0.8930, which is superior to other computing models of 0.8675 (IILLS), 0.7959 (BRWH), 0.7577 (LapRLS), and 0.7128 (CMF). Furthermore, the Leave One Out Cross Validation (LOOCV) experimental results also show that NERLS can achieve the AUC values of 0.9210, which is better than other models (IILLS: 0.9061, BRWH: 0.8105, LapRLS: 0.7713, CMF: 0.7491). In addition, a case study also confirms the effectiveness of NERLS in predicting potential virus-receptor interactions.
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