Immunoinformatics analysis of antigenic epitopes and designing of a multi-epitope peptide vaccine from putative nitro-reductases of Mycobacterium tuberculosis DosR

Abstract

Mycobacterium tuberculosis (Mtb) resides in alveolar macrophages as a non-dividing and dormant state causing latent tuberculosis. Currently, no vaccine is available against the latent tuberculosis. Latent Mtb expresses ~48 genes under the control of DosR regulon. Among these, putative nitroreductases have significantly high expression levels, help Mtb to cope up with nitrogen stresses and possess antigenic properties. In the current study, immunoinformatics methodologies are applied to predict promiscuous antigenic T-cell epitopes from putative nitro-reductases of the DosR regulon. The promiscuous antigenic T-cell epitopes prediction was performed on the basis of their potential to induce an immune response and forming a stable interaction with the HLA alleles. The highest antigenic promiscuous epitopes were assembled for designing an in-silico vaccine construct. A TLR-2 agonist Phenol-soluble modulin alpha 4 was exploited as an adjuvant. Molecular docking and Molecular Dynamics Simulations were used to predict the stability of vaccine construct with the immune receptor. The predicted promiscuous epitopes may be helpful in the construction of a subunit vaccine against latent tuberculosis, which can also be administered along with the BCG to increase its efficacy. Experimental validation is a prerequisite for the in-silico designed vaccine construct against TB infection.