Computational Analysis to Predict Drug Targets for the Therapeutic Management of Mycobacterium avium sub. Paratuberculosis

Abstract

Background: Mycobacterium avium sp. paratuberculosis (MAP) is a pathogen, which causes paratuberculosis in animals; it has also been found to be associated with a number of autoimmune disorders in humans. The emergence of drug resistance has also been found in this bacillus during disease management.

Objective: The present study's focus was to identify potential therapeutic targets for the therapeutic management of Mycobacterium avium sp. paratuberculosis infection by in silico analysis.

Methods: Differentially-expressed genes (DEGs) can be good drug targets, which can be identified from microarray studies. We used gene expression profile GSE43645 to identify differentiallyexpressed genes. An integrated network of upregulated DEGs was constructed with the STRING database and the constructed network was analyzed and visualized by Cytoscape. Clusters in the proteinprotein interaction (PPI) network were identified by the Cytoscape app ClusterViz. MAP proteins predicted in clusters were analyzed for their non-homology with the human proteins, and homologous proteins were excluded. Essential proteins and cellular localization analysis and the physicochemical characteristics prediction were also done. Finally, the druggability of the target proteins and drugs that can block the targets was predicted using the DrugBank database and confirmed by molecular docking. Structural prediction and verification of drug target proteins were also carried out.

Results: Two drug targets, MAP_1210 (inhA) and MAP_3961 (aceA), encoding enoyl acyl carrier protein reductase and isocitrate lyase enzymes, respectively, were finally predicted as potential drug targets.

Conclusion: Both of these proteins have been predicted as drug targets in other mycobacterial species also, supporting our results. However, further experiments are required to confirm these results.

Keywords: Mycobacterium avium sub. paratuberculosis; PPI; autoimmune disease; clusters; drug target; in silico analysis.