Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

An exploration on the topologies of SARS-CoV-2/human protein-protein interaction network

Network biology is an important finding that uncovers the significant elements in viral infection control. Since viruses use the proteins on their surfaces to attach and enter into the host cell, the establishment of virus-host protein interactions is a potent regulator of the global organization of the viral life cycle after virus entry into host cells. In this instance, a topological study on the SARS-CoV-2/Human Protein-Protein Interaction Network (PPIN) evacuates much information about the protein-protein interactions. By making some interruptions to the interaction between proteins and hosts, we can quickly reduce the spread of the disease and get an insight into the target protein for drug development. This paper mainly focused on the graphical and structural complexity of the SARS-CoV-2/Human PPIN. For this purpose, the various primary (distance, radius, diameter, etc…) and advanced levels of graph measures (density, modularity, clustering coefficient, etc…) as well as a few fractal (box dimension, multifractal analysis) and entropy measures have been used. In addition, several graph descriptions and distribution graphs of PPIN offered to gain a thorough understanding of the SARS-CoV-2/Human PPIN. Conclusively, based on our work, we have discovered that PPIN is moderately complex and identified that hiring Nsp8 as a target node will positively affect the PPIN and has pointed out that mathematically found target proteins are matched with already suggested target proteins in the previous survey.Communicated by Ramaswamy H. Sarma.