Comprehensive immunoinformatics guided design and in silico assessment of a multi-epitope vaccine to elicit immunity against Mayaro virus.

Abstract

The Mayaro virus (MAYV), an emerging arbovirus of the Alphavirus genus (family Togaviridae), shares clinical and epidemiological features with other arboviruses such as Chikungunya, Dengue, and Zika. It causes Mayaro fever in humans, characterized by febrile illness and prolonged arthralgia, often leading to misdiagnosis. Given the absence of licensed vaccines or antiviral therapies and the presence of competent vectors in endemic regions, MAYV poses a significant threat of epidemic, particularly in rural South America. In this research, computational immunological methods were employed to construct a polypeptide vaccine, with a focus on the structural polyprotein of the Mayaro virus. A series of rigorous computational assessments were used to predict the most potent T and B lymphocyte epitopes for the vaccine candidate. The vaccine exhibited favorable physicochemical properties, stable secondary and tertiary structures, and high solubility. Molecular dynamics simulations demonstrated its structural stability, while docking studies indicated robust interactions with TLR2 and TLR4 receptors, essential for initiating immune responses. Codon optimization, along with in silico cloning, demonstrated efficient expression of the vaccine candidate in an E. coli system. Finally, an immune simulation indicated that the vaccine candidate could induce a robust and long-lasting immune response.

Supplementary information: The online version contains supplementary material available at 10.1007/s40203-025-00387-5.