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

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.

In Silico Prediction of Epitopes in Virulence Proteins of Mycobacterium ulcerans for Vaccine Designing.

Background: Mycobacterium ulcerans is the fundamental agent of the third most common Mycobacterial disease known as Buruli Ulcer (BU). It is an infection of the skin and soft tissue affecting the human population worldwide. Presently, the vaccine is not available against BU. Objective: This study aimed to investigate the vaccine potential of virulence proteins of M. ulcerans computationally. Methods: Chromosome encoded virulence proteins of Mycobacterium ulcerans strain Agy99 were selected, which were available at the VFDB database. These proteins were analyzed for their subcellular localization, antigenicity, and human non-homology analysis. Ten virulence factors were finally chosen and analyzed for further study. Three-dimensional structures for selected proteins were predicted using Phyre2. B cell and T cell epitope analysis was done using methods available at Immune Epitope Database and Analysis Resource. Antigenicity, allergenicity, and toxicity analysis were also done to predict epitopes. Molecular docking analysis was done for T cell epitopes, those showing overlap with B cell epitopes. Results: Selected virulence proteins were predicted with B cell and T cell epitopes. Some of the selected proteins were found to be already reported as antigenic in other mycobacteria. Some of the predicted epitopes also had similarities with experimentally identified epitopes of M. ulcerans and M. tuberculosis which further supported our predictions. Conclusion: In-silico approach used for the vaccine candidate identification predicted some virulence proteins that could be proved important in future vaccination strategies against this chronic disease. Predicted epitopes require further experimental validation for their potential use as peptide vaccines.