In silico Designing of a Multiepitope Vaccine using the Hallmark Proteins of Mycobacterium tuberculosis Involved in Different Aspects of Virulence.
Аннотация
BACKGROUND
Mycobacterium tuberculosis ( M.tb ) remains a leading global cause of mortality. The current Bacillus Calmette-Guérin vaccine lacks efficacy in adults and fails to generate a long-term memory response. With the rise of multidrug-resistant strains, there is an urgent need for novel vaccines that can provide broader protection. This study aimed to design a multiepitope vaccine (MEV) targeting hallmark proteins involved in different aspects of M.tb virulence.
METHODS
Four unique M.tb proteins, Rv1507A (role in memory response), Rv1509 (role in phagolysosomal escape), Rv1954A (role in macrophage activation/antigen presentation), and Rv2231A (role in persistence) were selected. In silico analyses were performed to identify epitopes with high-binding affinity for Toll-like receptors (TLRs). Two MEVs were optimized for codon and were linked with adjuvants that could bind with TLR4 or TLR2 (TLR4-laterosporulin and TLR2-PorB). Physicochemical properties, allergenicity, toxicity, and structural stability were evaluated, followed by molecular docking with TLR receptors, molecular dynamic (MD) simulation, in silico cloning, and immune simulations.
RESULTS
Both MEVs exhibited favorable biophysical properties and high structural stability. Molecular docking confirmed strong binding affinities with TLR2 and TLR4 receptors, suggesting a robust activation of innate and adaptive immunity. Immunological simulations predicted a potent immune response characterized by high cytokine production and memory cell differentiation. The designed MEV demonstrated approximately 90% global population coverage.
CONCLUSIONS
The designed MEVs effectively bridge gaps in existing TB immunization by targeting multiple aspects of M.tb pathogenesis. These in silico leads provide a promising framework for preclinical studies, potentially moving toward a more effective clinical solution against TB.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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