In Silico Designing of a Multitope Vaccine against Rhizopus microsporus with Potential Activity against Other Mucormycosis Causing Fungi.

During the current era of the COVID-19 pandemic, the dissemination of Mucorales has been reported globally, with elevated rates of infection in India, and because of the high rate of mortality and morbidity, designing an effective vaccine against mucormycosis is a major health priority, especially for immunocompromised patients. In the current study, we studied shared Mucorales proteins, which have been reported as virulence factors, and after analysis of several virulent proteins for their antigenicity and subcellular localization, we selected spore coat (CotH) and serine protease (SP) proteins as the targets of epitope mapping. The current study proposes a vaccine constructed based on top-ranking cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell lymphocyte (BCL) epitopes from filtered proteins. In addition to the selected epitopes, β-defensins adjuvant and PADRE peptide were included in the constructed vaccine to improve the stimulated immune response. Computational tools were used to estimate the physicochemical and immunological features of the proposed vaccine and validate its binding with TLR-2, where the output data of these assessments potentiate the probability of the constructed vaccine to stimulate a specific immune response against mucormycosis. Here, we demonstrate the approach of potential vaccine construction and assessment through computational tools, and to the best of our knowledge, this is the first study of a proposed vaccine against mucormycosis based on the immunoinformatics approach.