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A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing

Authors: Habibi M; Taheri G; Aghdam R;

A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing

Abstract

AbstractThe Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus needs a fast recognition of effective drugs to save lives. In the COVID-19 situation, finding targets for drug repurposing can be an effective way to present new fast treatments. We have designed a two-step solution to address this approach. In the first step, we identify essential proteins from virus targets or their associated modules in human cells as possible drug target candidates. For this purpose, we apply two different algorithms to detect some candidate sets of proteins with a minimum size that drive a significant disruption in the COVID-19 related biological networks. We evaluate the resulted candidate proteins sets with three groups of drugs namely Covid-Drug, Clinical-Drug, and All-Drug. The obtained candidate proteins sets approve 16 drugs out of 18 in the Covid-Drug, 273 drugs out of 328 in the Clinical-Drug, and a large number of drugs in the All-Drug. In the second step, we study COVID-19 associated proteins sets and recognize proteins that are essential to disease pathology. This analysis is performed using DAVID to show and compare essential proteins that are contributed between the COVID-19 comorbidities. Our results for shared proteins show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases.

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Keywords

SARS-CoV-2, Science, Q, R, Drug Repositioning, COVID-19, Antiviral Agents, Article, COVID-19 Drug Treatment, Drug Delivery Systems, Host-Pathogen Interactions, Medicine, Humans, Protein Interaction Maps, Signal Transduction

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Top 10%
Green
gold