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
HEK 293 cells published by Gordon et al. (
Nature
2020
,
583
, 459–468) 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 PPI networks 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 PPI networks. 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 PPI networks.
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.
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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