[Submitted on 5 May 2020]
Abstract: COVID-19 has created a global pandemic with high morbidity and mortality in
2020. Novel coronavirus (nCoV), also known as Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV2), is responsible for this deadly disease.
International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is
highly genetically similar to SARS-CoV epidemic in 2003 (89% similarity).
Limited number of clinically validated Human-nCoV protein interaction data is
available in the literature. With this hypothesis, the present work focuses on
developing a computational model for nCoV-Human protein interaction network,
using the experimentally validated SARS-CoV-Human protein interactions.
Initially, level-1 and level-2 human spreader proteins are identified in
SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible
(SIS) model. These proteins are considered as potential human targets for nCoV
bait proteins. A gene-ontology based fuzzy affinity function has been used to
construct the nCoV-Human protein interaction network at 99.98% specificity
threshold. This also identifies the level-1 human spreaders for COVID-19 in
human protein-interaction network. Level-2 human spreaders are subsequently
identified using the SIS model. The derived host-pathogen interaction network
is finally validated using 7 potential FDA listed drugs for COVID-19 with
significant overlap between the known drug target proteins and the identified
spreader proteins.
Submission history
From: Subhadip Basu [view email]
[v1]
Tue, 5 May 2020 04:16:21 UTC (1,346 KB)
