Designing of multi-valent epitope vaccine displaying interactions with diverse HLA alleles against Candida auris using immuno-informatics.

The emergence of multidrug resistanceagainst several antifungal drugs and the absence of alternate therapy limits the treatment choices leading to the spread of Candida auris infections, especially inimmunocompromised patients. This work aims to construct the multi-epitope vaccine using an immuno-informatics approachdue to the lack of efficient treatments for C. auris. Very few in-silico studies have been conducted to design a vaccine against C. auris majorly targeting the specific proteins regardless of the importance of non-structural proteins. The whole proteome was targeted to identify the antigenic proteins because components other than non-structural proteins can also potentially act as immunogens. The antigenic determinants were mapped in the target proteins and screened via IEDB analysis and prediction tools. Distinctive HLA types manifested at varied genotypic frequencies in diverse ethnicities. Therefore, to design an effectual vaccine construct, the candidate T-cell antigenic determinants were employed for population coverage. Various bioinformatics tools and servers were used for the 3D analysis of vaccine structure, including prediction, refinement, and validation. The computational validation of the molecular interaction of the proposed vaccine with TLR4, TLR5, HLA-A*11:01, and HLA-A*02:01 was done using docking studies. The docked complexes were subjected to molecular dynamics (MD) simulations to confirm their stability, compactness, and flexibility. Simulation studies demonstrated that the vaccine complexed with immune and MHC receptors was stable during the simulation time. The outcome of the current study suggests the designed vaccine can be a potential vaccine candidate and elicit the immune response against C. auris. However, experimental verification (in-vitro/in-vivo) is required to confirm the effectiveness and safety of the designed vaccine construct.