Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features

Abstract

BackgroundSARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 183 million individuals and caused about 4 million deaths globally. A protein-protein interaction network (PPIN) and its analysis can provide insight into the behavior of cells and lead to advance the procedure of drug discovery. The identification of essential proteins is crucial to understand for cellular survival. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common proteins. Analyzing influential proteins and comparing these networks together can be an effective step helping biologists in drug design. ResultsWe used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. PCA-based dimensionality reduction was applied on normalized centrality values. Some measures demonstrated a high level of contribution in comparison to others in both PPINs, like Barycenter, Decay, Diffusion degree, Closeness (Freeman), Closeness (Latora), Lin, Radiality, and Residual. Using validation measures, the appropriate clustering method was chosen for centrality measures. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. ConclusionsThrough analysis and comparison, both networks exhibited remarkable experimental results. The network diameters were equal and in terms of heterogeneity, SARS-CoV-2 PPIN tends to be more heterogeneous. Both networks under study display a typical power-law degree distribution. Dimensionality reduction and unsupervised learning methods were so effective to reveal appropriate centrality measures.