Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

Abstract

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19

“…All the Venn diagrams and minimal triangular matrix are generated through the multi-list comparator tool of molbiotool [ 50 ]. Further analyses have been carried out, and the prediction of each model has been compared with the other in-silico methodologies of Tehrani et al [ 36 ], Barman et al [ 1 ], Chen et al [ 33 ], and Saha et al [ 34 ]. The results are highlighted in Figure 5 , Figure 6 and Figure 7 .…”

“…The top three machine learning model predictors out of eight are now used to predict the COVID-19 human drug targets from the remaining 204,871 human proteins. The predicted COVID-19 human drug targets are further validated with the other COVID-19 human drug targets detected through several in-silico and in-vitro methodologies as specified in various works of literature [ 1 , 33 , 34 , 35 , 36 ]. A significant overlap of the predicted targets with the others has been observed, which motivates us to analyze those COVID-19 human drug targets further, which are not overlapped/matched.…”

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

“…A modification of the average shortest path length was used in ref ( 679 ). Saha et al 680 employed various network measures to identify the spreader nodes in the SARS-CoV-human protein–protein interaction network, hoping to find possible lineage with the disease propagation pattern of the COVID-19 pandemic.…”

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human–Coronavirus Family Interactome

“…The primary factor which needs to be considered while examining the disease transmission process from SARS-CoV-2 to humans is the Protein–Protein Interaction Network (PPIN). It is critical for determining essential proteins and functions [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] responsible for various diseases. The primary focus of research has changed from the study of the PPIN underlying various types of human diseases to the study of the PPIN due to the improvement in the availability of human PPIN data [ 20 ].…”