Kumar, N.; Mukhtar, M.S. Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study. Data2024, 9, 101.
Kumar, N.; Mukhtar, M.S. Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study. Data 2024, 9, 101.
Kumar, N.; Mukhtar, M.S. Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study. Data2024, 9, 101.
Kumar, N.; Mukhtar, M.S. Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study. Data 2024, 9, 101.
Abstract
Network centrality analyses have proven successful in identifying important nodes in diverse host-pathogen interactomes. The current study presents a comprehensive investigation of the human interactome and SARS-CoV-2 host targets. We first constructed a comprehensive human interactome by compiling experimentally validated protein-protein interactions (PPIs) from eight distinct sources. Additionally, we compiled a comprehensive list of 1,449 SARS-CoV-2 host proteins and analyzed their interactions within the human interactome, which identified enriched biological processes and pathways. Seven diverse topological features were employed to reveal the enrichment of SARS-CoV-2 targets in the human interactome, with Load centrality emerging as the most effective metric. Furthermore, a novel approach called CentralityCosDist was employed to predict SARS-CoV-2 targets, which proved effective in expanding the pool of predicted targets. Pathway enrichment analyses further elucidated the functional roles and potential mechanisms associated with predicted targets. Overall, this study provides valuable insights into the complex interplay between SARS-CoV-2 and the host cellular machinery, contributing to a deeper understanding of viral infection and immune response modulation.
Keywords
comprehensive human interactome; SARS-CoV-2 targets; network metrices; centrality analyses
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.