Subtractive proteomics assisted therapeutic targets mining and designing ensemble vaccine against Candida auris for immune response induction

The emergence of variants and the reports of co-infection caused by Candida auris in COVID-19 patients adds a further complication to the global pandemic situation. To date, no effective therapy is available for C. auris infections. Thus, characterization of therapeutic targets and designing effective vaccine candidates using sub-tractive proteomics and immune-informatics approaches is useful tool in controlling the emerging infections associated with SARS-CoV-2. In the current study, subtractive proteomics-assisted annotation of the vaccine targets was performed, which revealed seven vaccine targets. An immunoinformatic-driven approach was then employed to map protein-specific and proteome-wide immunogenic peptides (CTL, B cell, and HTL) for the design of multi-epitope vaccine candidates (MEVCs). The results demonstrated that the vaccine candidates possess strong antigenic features (>0.4 threshold score) and are classified as non-allergenic. Validation of the designed MEVCs through molecular docking, in-silico cloning, and immune simulation further demonstrated the efficacy of the vaccines by producing immune factor titers (ranging from 2500 to 16000 au/mL) i.e., IgM, IgG, IL -6, and Interferon-alpha. In conclusion, the current study provides a strong impetus in designing anti-fungal strategies against Candida auris.

Automated summary

This study uses subtractive proteomics and immune-informatics approaches to design multi-epitope vaccine candidates against Candida auris. The designed vaccines demonstrate strong antigenic features and non-allergenic properties, and show efficacy in immune response. This is of significant importance for designing strategies to counter Candida auris infections.