In silico prediction of multi-epitope vaccine candidates against Mycobacterium leprae

Background Leprosy, also known as Hansen's disease, is an infectious disease caused by Mycobacterium leprae. Despite ongoing efforts to control the disease, leprosy remains a global health concern, with Indonesia ranking third in the world for the highest number of cases. Objective This study aims to identify epitopes that can induce T and B cell immune responses through an in silico approach, to design a multi-epitope vaccine candidate against Mycobacterium leprae. Methods The study used an in silico vaccine design approach utilizing ESAT6, Ag85B, ML2028, ML2380, and ML2055 proteins from Mycobacterium leprae. The process involved sequence alignment, T cell (CTL and HTL) and B cell epitopes identification, and antigenicity, allergenicity, and toxicity assessment. Selected epitopes were constructed into a multi-epitope vaccine candidate using linkers. The tertiary structure of the vaccine was modeled with AlphaFold and evaluated via Prosa-web. The stability and interaction between the vaccine candidate and TLR4 were analyzed using molecular docking. Results The vaccine candidate demonstrated stable interactions with TLR4, with a binding free energy of -13.9 kcal/mol. The vaccine candidate was also predicted to be stable, antigenic, non-allergenic, non-toxic, and hydrophilic. Conclusion This in silico design of a multi-epitope vaccine candidate shows potential for development as a vaccine against leprosy.