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Respiratory syncytial virus (RSV) poses a significant global health threat, especially among vulnerable populations such as infants and older people. Developing an effective vaccine against RSV remains a critical endeavor. This research paper presents an innovative approach to vaccine candidate design using immunoinformatics and computational approaches. Leveraging computational tools, we analyzed the structural proteins of both subtypes of RSV to identify potential helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes. Various selection criteria were analyzed computationally, such as antigenicity, allergic potential, toxicity, and ability to elicit cytokine synthesis of the epitopes. Additionally, the conservancy of the epitopes in different strains of RSV belonging to both subtypes was analyzed. These computational analyses led to the selection of six Major Histocompatibility Complex class I (MHC-I) binding, five MHC-II binding, and three B-cell epitopes linked with adjuvants for the final vaccine candidate. Moreover, molecular docking and molecular dynamics simulations have determined the stable interactions between the predicted RSV vaccine construct and the human Toll-Like Receptors (TLR4 and TLR5). Notably, the results revealed enhanced interactions and stability of the vaccine construct with the TLR4 complex compared to TLR5. Subsequently, immune simulation analysis has demonstrated the potential of the candidate RSV vaccine to induce a robust immune response. This study presents a promising vaccine against RSV, exhibiting favorable immunological and physicochemical attributes. The findings provide theoretical support for RSV vaccine development. While the outcomes of these in silico experiments are promising, further validation is essential through additional in vivo experiments.
Nguyen et al. (Tue,) studied this question.