Graph analysis and clustering of proteins linked with COVID-19

Abstract

A remarkable number of scientific initiatives are in practice to encounter the new coronavirus epidemic (COVID-19). One of the biggest challenges faced by the COVID-19 researchers in the therapeutic field is the knowledge about the biological functions in disease-human interacting proteins. The detection of COVID-19 protein complexes, a group of proteins that possess the same biological functions, helps in better understanding of the biological processes in our body. The main contribution of this work is to cluster proteins that perform the same biological functions to increase the knowledge about the COVID-19 disease-human interacting proteins. The authors investigated proteins linked with COVID-19 disease by creating a disease-human protein-protein interaction graph. Topological means of graph analysis and graph clustering have been employed to group proteins that possess the same biological functions. These clusters will be the protein complexes that work together to carry out a specific biological function in a human cell. Moreover, through the cluster analysis, we can uncover previously unknown COVID-19 disease-human protein links that are beneficial for promising knowledge discovery. Also, the authors evaluated how the Markov Cluster algorithm, a graph-based algorithm finds interesting patterns of similar features from COVID-19 disease-human protein-protein interaction graph. The Markov Cluster algorithm results in six statistically significant protein clusters, including cluster (A): keratinization (3.50E-71), (B): regulation of cellular process (6.62E-05), (C): regulation of cell cycle (1.31E-27), (D): mitotic cell cycle (1.66E-06), (E): regulation of phosphoprotein phosphatase activity (1.15E-09), and (G): G2/M transition of mitotic cell cycle (3.03E-07).