Detection of spreader nodes and ranking of interacting edges in Human-SARS-CoV protein interaction network

Abstract

The entire world has recently witnessed the commencement of coronavirus disease 19 pandemic. It is caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). It has exploited human vulnerabilities to coronavirus outbreak. SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure which ultimately puts an end to human life. No proven vaccine for n-CoV is available till date in spite of significant research efforts worldwide. International Committee on Taxonomy of Viruses (ICTV) has reached to a consensus that the virus SARS-CoV-2 is highly genetically similar to Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) outbreak of 2003. It has been reported that SARS-CoV has ~89% genetic similarities with n-CoV. With this hypothesis, the current work focuses on the identification of spreader nodes in SARS-CoV protein interaction network. Various network characteristics like edge ratio, neighborhood density and node weight have been explored for defining a new feature spreadability index by virtue of which spreader nodes and edges are identified. The selected top spreader nodes having high spreadability index have been also validated by Susceptible-Infected-Susceptible (SIS) disease model. Initially, the proposed method is applied on a synthetic protein interaction network followed by SARS-CoV-human protein interaction network. Hence, key spreader nodes and edges (ranked edges) are unmasked in SARS-CoV proteins and its connected level 1 and level 2 human proteins. The new network attribute spreadability index along with generated SIS values of selected top spreader nodes when compared with the other network centrality based methodologies like Degree centrality (DC), Closeness centrality (CC), Local average centrality (LAC) and Betweeness centrality (BC) is found to perform relatively better than the existing-state-of-art.

Is Fostamatinib a possible drug for COVID-19? – A computational study

“…The entire working mechanism for identifying Fostamatinib as one of the potential drug for COVID19 treatment can be categorized in 4 major sections: 1) detection of spreader nodes 37 in Human-nCoV PPIN 2) identification of potential FDA approved drugs with respect to COVID19 spreader nodes using Human-nCoV interaction network analysis 3) identification of potential candidate FDA drugs with respect to COVID-19 spreader nodes using COVID-19 symptoms, risk factors and clinical outcome based analysis 4) computational docking of potential drugs with respect to COVID19 protein structures.…”

“…Not every protein in a PPIN is a spreader protein/node. Spreader proteins are considered to be those specific proteins which have unique fast capability of transmitting infection in its neighbourhood in a short period of time 37 . They are identified through spreadability index computed by the combination of three terminologies: 1) Edge ratio 56 2) Neighbourhood density 56 and 3) node weight 57 .…”

“…The selected spreader nodes are also validated by Susceptible-Infected-Susceptible (SIS) model 33 (see Figure 1). This results in the formation of a PPIN which consists of 7 SARS-CoV, 24 level 1 and 111 level 2 human spreader proteins respectively under low threshold 37 . The potential Human-nCoV interactions have been identified using developed in silico fuzzy PPI model 58 .…”

“…• Recently we have developed an in silico model to identify potential spreader proteins in a Human-nCoV interaction network 37,38 . • The developed model was validated using the target proteins of the potential FDA drugs 34 for COVID-19 treatment.…”

Computational modeling of human-nCoV protein-protein interaction network

“…• Recently we have developed a computational model to identify potential spreader proteins in a Human-SARS CoV interaction network using SIS model 21 .…”