Immunoinformatic Design and Evaluation of a Multi-Epitope mRNA Vaccine RP14914P Targeting Latent Tuberculosis...

Area of Science:

• Vaccinology
• Immunoinformatics
• Computational Biology

Background:

• Latent tuberculosis infection (LTBI) is the primary reservoir for active tuberculosis cases.
• The Bacillus Calmette-Guérin vaccine is ineffective at preventing LTBI reactivation.
• A significant gap exists in preventive strategies for tuberculosis.

Purpose of the Study:

• To design a novel vaccine candidate using immunoinformatics and reverse vaccinology.
• To identify and characterize epitopes for T-cell and B-cell responses.
• To predict the immunogenicity, safety, and population coverage of the designed vaccine.

Main Methods:

• Applied an immunoinformatics/reverse-vaccinology pipeline to dormancy-related antigens.
• Predicted T-cell and B-cell epitopes using NetMHCI/IIpan-4.1 and ABCpred.
• Evaluated antigenicity, allergenicity, and toxicity; modeled structures; performed molecular docking and dynamics simulations; simulated immune responses; and optimized mRNA sequence.

Main Results:

• Developed RP14914P, a 866-amino acid polypeptide encoding multiple T-cell and B-cell epitopes.
• Predicted high antigenicity (0.7797) and immunogenicity (8.58629) with no predicted toxicity or allergenicity.
• Achieved broad global population coverage (82.35% HLA-I, 99.67% HLA-II) and predicted stable binding to TLR2/4 with potent immune activation.

Conclusions:

• In silico analyses suggest RP14914P is a promising mRNA vaccine candidate for preventing LTBI reactivation.
• The vaccine construct demonstrates favorable predicted immunogenicity, safety, and broad population coverage.
• Experimental validation is crucial to confirm the immunogenicity and protective efficacy of RP14914P.