bioRxivImmunology
Computational identification and characterization of antigenic properties of Rv3899c of Mycobacterium tuberculosis and its interaction with Human leukocyte antigen (HLA)
Abstract
Tuberculosis (TB) is the second largest infectious disease that kills 1.2 million people annually worldwide. A rise in drug-resistant TB cases demands continued efforts towards the discovery & development of drugs and vaccines. In the recent past, though novel drugs have been added to the current TB regimen [1], research on new vaccine candidates needs a greater thrust. Secretory proteins of H37Rv are frequently studied for their antigenicity and their scope as protein subunit vaccines decrees further analysis. In this study, Rv3899c of H37Rv emerges as a potential vaccine candidate on its evaluation by several bioinformatics tools. It is a non-toxin, secretory protein with an immunoglobulin-like fold which does not show similarity with a human protein. We found Rv3899c homologs in several mycobacterial species and its antigenic score (0.54) to compare well with the known immunogens such as ESAT-6 (0.56) and Rv1860 (0.52). Structural examination of Rv3899c predicted ten antigenic peptides, an accessibility profile of the antigenic determinants constituting B-cell epitope rich regions and a low Abundance of Antigenic Regions (AAR) value. Significantly, our study shows ESX-2 secretion system proteins and antigenic PE/PPE proteins of H37Rv as the interacting partners of Rv3899c. Further, molecular docking predicted Rv3899c to interact with human leukocyte antigen HLA-DRB1*04:01 through its antigenically conserved motif (RAAEQQRLQRIVDAVARQEPRISWAAGLRDDGTT). Interestingly, the binding affinity was observed to increase on citrullation of its Arg1 residue. Taken together, the computational characterization and predictive information suggest Rv3899c to be a promising TB vaccine candidate, which should be examined and validated experimentally.
- organism: human
- organism: mouse
- assay: proteomics
- research focus: host-pathogen & immune response
- approach: biophysical modeling
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