2020/05/05 by Sovan Saha, Saha, Sovan, Anup Kumar Halder +9
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Molecular Networks (q-bio.MN) #Protein Structure and Dynamics
paper · pdf · doi:10.48550/arxiv.2005.04108
openalex publication_date 2020/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.