An immunoinformatics-based designed multi-epitope candidate vaccine against Mycoplasma pneumoniae

Abstract

Background: Mycoplasma pneumoniae is a common cause of respiratory infections, and the emergence of antibiotic resistance underscores the need for an effective vaccine. This study employed an in silico approach to design a multi-epitope vaccine using genomic data from M. pneumoniae. Methods: Candidate antigens were screened based on antigenicity, allergenicity, and toxicity. A non-allergenic, non-toxic antigen was selected to design a construct comprising B-cell and T-cell epitopes, fused with the cholera toxin B subunit as an adjuvant and connected via immunologically favorable linkers. The construct was evaluated through molecular docking, molecular dynamics simulations, binding free energy calculations (MM/GBSA), immune simulation, codon optimization, and in silico cloning. Results: The vaccine showed strong binding to TLR4 with a predicted binding energy of − 21.7 kcal/mol. Structural validation yielded a ProSA Z-score of − 5.71, an ERRAT score of 84.188%, and 98.7% of residues in favored or allowed regions of the Ramachandran plot. Immune simulations indicated elevated IFN-γ and antibody levels, and population coverage analysis showed global HLA representation of 50.69%. Conclusions: The final vaccine construct demonstrated structural stability, strong immune receptor interactions, and broad population coverage in silico, supporting its potential efficacy against M. pneumoniae and warranting further experimental validation.