Designing a multi-serotype Dengue virus vaccine: an in silico approach to broad-spectrum immunity

Abstract

Dengue virus infection represents a major global health issue, with four distinct serotypes complicating the challenge of developing a vaccine due to the need for balanced, long-lasting immunity against all serotypes. Current vaccines have limitations, including an increased risk of severe dengue in seronegative individuals and moderate efficacy, highlighting the need for more effective solutions. Our study aimed to design a multi-serotype Dengue virus vaccine using a computational approach to achieve broad-spectrum immunity. We employed advanced computational tools and algorithms to predict B-cell and T-cell epitopes, ensuring the selection of antigenic targets that provide comprehensive protection against all four serotypes. The methodology included tools for B-cell epitope prediction, tools for MHC class II and I peptide predictions, and tools for toxicity and allergenicity screening to ensure the safety of the vaccine candidates. Our results identified 21 B-cell epitopes, 15 CTL peptides, and 12 HTL peptides, validated for safety regarding toxicity and allergenic potential. The vaccine construct incorporated the adjuvant {beta}-defensin-3 and specific linkers to enhance immunogenicity and stability. Tertiary structure prediction, Ramachandran plot analysis, and stereochemical examination confirmed the stability and quality of the vaccine model. These findings demonstrate the potential of computational methods in addressing the complex challenges of Dengue virus vaccine development. Our computational approach offers a novel pathway for vaccine design, potentially accelerating the development of effective multi-serotype vaccines. This study provides a promising foundation for future research and clinical validation, marking a significant step forward in dengue vaccine development.