Multiscale statistical physics of the pan-viral interactome unravels the systemic nature of SARS-CoV-2 infections

Abstract

Protein–protein interaction networks have been used to investigate the influence of SARS-CoV-2 viral proteins on the function of human cells, laying out a deeper understanding of COVID–19 and providing ground for applications, such as drug repurposing. Characterizing molecular (dis)similarities between SARS-CoV-2 and other viral agents allows one to exploit existing information about the alteration of key biological processes due to known viruses for predicting the potential effects of this new virus. Here, we compare the novel coronavirus network against 92 known viruses, from the perspective of statistical physics and computational biology. We show that regulatory spreading patterns, physical features and enriched biological pathways in targeted proteins lead, overall, to meaningful clusters of viruses which, across scales, provide complementary perspectives to better characterize SARS-CoV-2 and its effects on humans. Our results indicate that the virus responsible for COVID–19 exhibits expected similarities, such as to Influenza A and Human Respiratory Syncytial viruses, and unexpected ones with different infection types and from distant viral families, like HIV1 and Human Herpes virus. Taken together, our findings indicate that COVID–19 is a systemic disease with potential effects on the function of multiple organs and human body sub-systems. Characterizing the interactions between viral and human proteins is key to understand the function and structure of viruses such as SARS-CoV-2 and for informing drug design and repurposing strategies. Here, the authors use statistical physics techniques to perform a systematic multiscale comparison of the effects on the human interactome of SARS-CoV-2 with respect to other viruses, and find that COVID-19 exhibits properties typical of systemic diseases.