In Silico prediction of multi-epitope vaccine candidates targeting chikungunya virus structural proteins in Malaysian populations

Chikungunya is a re-emerging mosquito-borne disease caused by the chikungunya virus (CHIKV). It typically causes fever, rash, arthralgia, and muscle pain, adversely affecting the quality of life and has socio-economic and health impacts in epidemic areas. Despite decades of efforts, there is no effective treatment or prevention for CHIKV, making it a significant health-related quality of life issue. Hence, this study proposes innovative immunoinformatic and bioinformatics-driven approaches to identify CHIKV antigens as promising targets for multi-epitope vaccine design for the Malaysian population. The analysis revealed a total of 31 epitopes that matched the most common HLA alleles in Malaysian populations. These epitopes met the criteria for multi-epitope vaccine candidates with high antigenicity, non-allergenic, non-toxicity, and sufficient stability characteristics. This study presents promising vaccine candidates with robust design and demonstrated immunogenicity to address chikungunya's global health burden.