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bioRxiv · 10.1101/2020.09.29.318931

Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks

Nadeau, R.·Shahryari Fard, S.·Scheer, A.·Hashimoto-Roth, E.·Nygard, D.·Abramchuk, I.·Chung, Y.-E.·Bennett, S. A. L.·Lavallee-Adam, M.
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Abstract

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK293 cells published by Gordon et al. to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on a PPI network generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in the network. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in the network. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

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Nadeau, R., Shahryari Fard, S., Scheer, A., Hashimoto-Roth, E., Nygard, D., Abramchuk, I., Chung, Y.-E., Bennett, S. A. L., Lavallee-Adam, M.. 2020-09-30. Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks. https://doi.org/10.1101/2020.09.29.318931

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