Developing a multi-epitope vaccine against MR766 strain of Zika virus: A bioinformatics approach

The Zika Virus has emerged as a significant global health concern, particularly due to its association with severe neurological complications such as microcephaly and Guillain-Barré syndrome. Given the urgent need for effective intervention strategies, this study aims to develop a targeted vaccine leveraging bioinformatics methodologies to identify B- and T-cell epitopes within the Zika virus Strain MR766 structural proteins. Through comprehensive epitope prediction algorithms, novel epitopes were identified and selected based on criteria including toxicity, immunogenicity, and antigenicity. These epitopes were then integrated into a multiple epitope vaccine constructs, incorporating various linker sequences (EAAAK, AAY, GPGPG) to optimize immunogenicity. Subsequent molecular docking simulations facilitated the design of a vaccine structure capable of effectively interacting with its target receptors. The vaccine construct was cloned into a pET-28a (+) vector for expression in Escherichia coli using SnapGene software. Evaluation of the expressed vaccine demonstrated a robust immune response in preclinical studies. Computational tools were employed to analyze the vaccine's efficacy against diverse Zika virus strains. The resulting Multi-Epitope Vaccine (MEV) emerges as a promising candidate for combating Zika virus infections, offering potential benefits in global public health efforts against this pathogen.