Design and in silico evaluation of an mRNA vaccine against HTLV-1 using AI-driven reverse vaccinology approaches

Abstract

Human T-lymphotropic virus type 1 (HTLV-1) is the first discovered human oncogenic retrovirus that can cause adult T-cell leukemia/lymphoma, HTLV-1-associated myelopathy/tropical spastic paraparesis, and several other diseases. Due to the poor prognosis of these diseases and the limited therapeutic modalities, the need for an HTLV-1 vaccine is crucial. The current study has used an artificial intelligence-driven reverse vaccinology approach to design an mRNA vaccine against HTLV-1. The two most antigenic proteins of the virus were selected and analyzed using multiple immunoinformatics tools to identify the antigenic immunodominant epitopes for T- and B-cells. Subsequently, the final selected epitopes and the adjuvant were connected using proper linkers. Subsequently, multiple 3D structures were modeled for the vaccine. After refining and evaluating the modeled structures, the best model was selected as the final candidate vaccine structure. The proposed mRNA structure is a potential vaccine with suitable immunological and physicochemical properties against HTLV-1. Docking and simulation analyses showed a proper interaction between the vaccine and the corresponding receptor of the employed adjuvant. However, additional experimental studies are required to further confirm the vaccine’s efficacy.