Immunoinformatics-based design and in silico evaluation of a multi-epitope vaccine candidate against Naegleria fowleri.

Abstract

Primary amoebic meningoencephalitis (PAM) is caused by Naegleria fowleri, a free-living amoeba. It is a fatal infection with more than 95% mortality rate. Even though several combinations of antimicrobial agents were used to treat the condition, the overall survival remains poor and no effective vaccine is currently available. In this study, an immunoinformatic approach was utilized for designing a multi-epitope vaccine against N. fowleri. The sequences of antigenic proteins were retrieved for epitope prediction. The computational prediction identified 400 CTL, 590 HTL, and 65 B-cell high-binding epitopes. After evaluating their antigenicity, allergenicity and toxicity, 5 CTL, 5 HTL and 3 B-cell epitopes were selected and assembled into a multi-epitope vaccine construct using linkers and a human β-defensin-3 adjuvant. The 3D structure of the vaccine was predicted using AlphaFold and refined with GalaxyRefine. Ramachandran plot analysis indicated that 96.5% of the residues of the refined vaccine are present in the most favored regions, indicating good stereochemical quality. The molecular docking analysis was performed using HADDOCK to evaluate the interaction between the vaccine and the Toll-like Receptors (TLRs), which predicted favorable binding affinities of -15.6 kcal/mol, -14.2 kcal/mol, and - 13.1 kcal/mol for TLR1 & 2, TLR3, and TLR4, respectively. The codon was optimized using JCat and cloned into the pET-28a(+) plasmid. The in silico immune simulation analysis predicted potential humoral and cellular immune responses to the vaccine construct. Overall, the proposed multi-epitope vaccine represents a promising computational candidate, however, further experimental validations have to be performed to confirm its efficacy against the pathogen.