In-Silico Identification of Structural Changes and Molecular Interactions of Mutations in Multiple Drug Resistant (MDR) Bacteria

In-Silico Vaccine Designing represents a paradigm shift in vaccine research, using computational tools to accelerate and refine the development process. By integrating bioinformatics, immunoinformatics, and structural biology, this approach enables researchers to identify and predict antigenic regions with high precision, reducing the time and cost associated with traditional vaccine development. Tuberculosis (TB) is a global health threat, making it necessary to develop innovative vaccine strategies. This study focuses on the insilico design of a multi-epitope vaccine targeting the integral transmembrane protein kefB of Mycobacterium tuberculosis. The amino acid sequence of kefB was retrieved from UniProt, and antigenic epitopes were identified using computational tools. B-cell epitopes were predicted via LB-tope - ABCpred, while T - cell epitopes (MHCI and MHC-II) were derived from IEDB. The best antigenic epitopes were selected based on their VaxiJen scores, followed by allergenicity prediction using AllerTOP. A vaccine construct was designed by linking the epitopes to a 50s ribosomal protein adjuvant using linkers. The construct was modified in 3D using SwissModel, visualized with Chimera, and validated through structural assessment tools, including the Ramachandran Plot via MOLProbity and refinement by GalaxyWEB. The final construct was docked with the Toll-like Receptor 2 (TLR2) of Homo sapiens using PatchDock to ensure receptor-ligand compatibility. Codon optimization was performed using JCat for improved expression, and a pET vector was designed for potential cloning. Host immune response simulation (CImmSim) and population coverage analysis (IEDB) confirmed the vaccine’s immunogenic potential. This in-silico approach highlights a promising strategy for vaccine development against tuberculosis and emphasizes the potential of computational biology in accelerating vaccine research thus revolutionizing health care.