Immunoinformatic design of a multiepitope vaccine construct based on proteins from Mycobacterium tuberculosis KZN 1435

Abstract

Tuberculosis (TB) caused by Mycobacterium tuberculosis remains a major global health burden, particularly with the emergence of multidrug-resistant (MDR) strains such as KZN 1435. In this study, a strain-specific multiepitope subunit vaccine candidate was rationally designed using an integrated immunoinformatic pipeline to support targeted vaccine development against MDR-TB. Candidate proteins from the M. tuberculosis KZN 1435 proteome were screened for secretory potential, transmembrane topology, and non-homology with human proteins. A total of 84 non-redundant secretory candidate proteins were retained after the initial secretory-protein filtering step and were further evaluated for downstream epitope prediction. Predicted 12-mer B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes were assessed for antigenicity, allergenicity, toxicity, and HLA-binding profiles, resulting in eight high-confidence epitope-protein entries corresponding to six non-redundant peptide sequences. After removal of duplicated peptide sequences and alignment with the final vaccine architecture used for downstream analyses, five non-redundant epitopes were assembled with an N-terminal adjuvant and optimized linkers to generate a 126-residue multiepitope construct with favorable predicted physicochemical properties, hydrophilicity, solubility, and theoretical immunogenic potential. Tertiary structure modeling suggested an acceptable folded conformation, while molecular docking indicated compatible interactions with TLR2 and TLR4 immune receptors. Codon optimization further supported the theoretical feasibility of recombinant expression in Escherichia coli. This strain-focused vaccine construct represents a promising computational candidate that requires further in vitro and in vivo validation before translational application against drug-sensitive and MDR M. tuberculosis infections.