Online analytics for host-pathogen protein interactions analysis

Abstract

As Influenza A virus (IAV) is a significant danger to global human health and life, it is critical to have a deeper knowledge of the virulence factors responsible for IAV infections to counteract potential outbreaks. A reliable analytical tool requires dependable data. This project started with data collection, where records of IAV infections from experiments conducted in mice were collected from numerous literature searches to conduct a meta-analysis, to deliver more accurate insights and offer adequate confidence on the viral factors accountable for the extreme harmfulness of IAV infections. Additionally, host-pathogen protein interaction analysis is intensified by the fact that an average protein consists of two or more domains, which are structurally and evolutionary independent subunits. As each domain has its distinct structure and biological function, only a selected subset of domains constituting each protein are involved in an interaction between a pair of proteins. Thus, superfamily domains from IAV and mouse proteins involved in host-pathogen protein interactions must be uncovered. Finally, a Graphical User Interface (GUI) was implemented to present all collected data, particularly, the virulence classification labels for records of IAV infections and to display interacting IAV-mouse protein domains as a network of nodes.