ArticleScientific reports2025
Design of a multi-Epitope mRNA vaccine against Brucella type IV secretion system using reverse vaccinology and immunogenicity approaches.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
10 authors.
Funding
Abstract
Brucellosis is a zoonotic disease caused by bacteria of the Brucella genus. This study investigates the development of a multi-epitope mRNA vaccine aimed at combating Brucella infections. The approach involves selecting the architecture of the pathogenic type IV secretion system (T4SS) of Brucella using bioinformatics tools and reverse vaccinology methodologies. The candidate proteins chosen for this vaccine include VirB2, VirB3, and VirB6, along with the effector protein BtpA. Key findings include robust antigenicity scores for the candidate proteins (VirB2:0.5685; VirB3:0.5329;VirB6:0.5054;BtpA:0.5575).A variety of tools were essential for identifying potential epitopes suitable for incorporation into the mRNA vaccine, as well as for utilizing appropriate linkers for docking. Molecular dynamics simulations confirmed stable interactions between the vaccine and TLR4, with a docking score of - 350.15. Furthermore, computer cloning methods were employed alongside simulated gel electrophoresis to ensure a comprehensive evaluation of the vaccine's properties. The results of this investigation indicate that the newly formulated mRNA vaccine effectively stimulates an immune response, presenting innovative strategies for prevention and management. Although comprehensive evaluations of vaccine design have been conducted using bioinformatics tools and molecular simulations, certain limitations still exist, primarily manifested in the reliance on computational predictions without validation from wet lab experiments. Future research must further verify the accuracy of computational results through laboratory experiments.
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