インフォマティクスと分子動力学シミュレーションを統合したシンドビスウイルスに対するマルチエピトープワクチン候補の開発

Sindbis virus (SINV), belonging to the genus Alphavirus, is the causative agent of Pogosta disease in humans. The clinical infection is characterized by fever, malaise, rash, myalgia, and arthralgia, which is generally self-limiting. Chronic infection with SINV triggers autoimmune conditions that lead to persistent arthritis. Despite its clinical relevance, no licensed vaccine is currently available for the prevention of SINV infection. To the best of our knowledge, this study presents the first in silico design and evaluation of a multi-epitope vaccine candidate against SINV. Using an integrated immunoinformatics framework, the SINV structural polyprotein was systematically screened, leading to the identification of twelve highly antigenic immunological hotspots, derived from both experimentally validated and computationally predicted B-cell and T-cell epitopes. These epitopes were rationally assembled into a 317-amino acid multi-epitope vaccine construct using suitable linkers and the human β-defensin 2 as an immunostimulatory adjuvant. The designed construct exhibited favorable antigenicity, non-toxicity, stability, and physicochemical properties. Molecular docking and molecular dynamics simulations demonstrated encouraging interactions between the vaccine construct and innate immune receptors TLR-2 and TLR-4, highlighting its potential to trigger immune responses. Immune simulation predicted robust humoral and cell-mediated immune responses, while codon optimization and in silico cloning into the pETite vector indicated expression feasibility in Escherichia coli K12. This work proposes a novel immunoinformatics and molecular dynamics-based vaccine design pipeline for Sindbis virus and presents a computationally validated multi-epitope vaccine candidate, providing a foundation for future experimental validation toward effective vaccine development.