Computational modeling of human-nCoV protein-protein interaction network.
Methods
; 203: 488-497, 2022 07.
Article
in English
| MEDLINE | ID: covidwho-1559797
ABSTRACT
Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (â¼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the 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 the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered 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 a â¼99.98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
SARS-CoV-2
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Methods
Journal subject:
Biochemistry
Year:
2022
Document Type:
Article
Affiliation country:
J.ymeth.2021.12.003
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