tetano
Editor, Senior Moderator
Methods
. 2021 Dec 10;S1046-2023(21)00273-5.
doi: 10.1016/j.ymeth.2021.12.003. Online ahead of print.
Computational Modeling of Human-nCoV Protein-Protein Interaction Network
Sovan Saha[SUP] 1 [/SUP], Anup Kumar Halder[SUP] 2 [/SUP], Soumyendu Sekhar Bandyopadhyay[SUP] 3 [/SUP], Piyali Chatterjee[SUP] 4 [/SUP], Mita Nasipuri[SUP] 5 [/SUP], Subhadip Basu[SUP] 6 [/SUP]
Affiliations
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 (∼ 89% 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 ∼99.98% 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: Drug Target; Fuzzy Model; Gene Ontology; High-Quality Interactions; Human-nCoV Interactions; Protein Interaction Network analysis; SARS-CoV2; Spreadability Index; Spreader Nodes; Susceptible-Infected-Susceptible Model.
. 2021 Dec 10;S1046-2023(21)00273-5.
doi: 10.1016/j.ymeth.2021.12.003. Online ahead of print.
Computational Modeling of Human-nCoV Protein-Protein Interaction Network
Sovan Saha[SUP] 1 [/SUP], Anup Kumar Halder[SUP] 2 [/SUP], Soumyendu Sekhar Bandyopadhyay[SUP] 3 [/SUP], Piyali Chatterjee[SUP] 4 [/SUP], Mita Nasipuri[SUP] 5 [/SUP], Subhadip Basu[SUP] 6 [/SUP]
Affiliations
- PMID: 34902553
- DOI: 10.1016/j.ymeth.2021.12.003
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 (∼ 89% 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 ∼99.98% 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: Drug Target; Fuzzy Model; Gene Ontology; High-Quality Interactions; Human-nCoV Interactions; Protein Interaction Network analysis; SARS-CoV2; Spreadability Index; Spreader Nodes; Susceptible-Infected-Susceptible Model.