SARS-CoV-2 is the novel coronavirus which caused the COVID-19 pandemic and infected more than 10 million victims and resulted in over 500,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading virus. This increasing rate and consequently increasing number of infected individuals necessitate an immediate development of proper diagnostic methods and effective treatments. To date, no specific treatment is detected for curing COVID-19. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host and replicate its genome. Accordingly, host-virus protein-protein interactions’ (PPI) identification could be useful in predicting the behavior of virus and designing antiviral drugs. Identification of host-virus PPIs with experimental approaches are so time consumable, and so computational approaches are usually applied. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The third layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the two first layers. For further analysis on the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and report the most central human proteins in human PPI network. Moreover, differentially expressed genes of two available GSE were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.

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