Adjuvant selection and optimization in the design of an epstein–barr virus–based multi-epitope vaccine against Burkitt lymphoma
Year: 2026
Type: supplementary-materials
Abstract: Background This study aimed to identify and optimize a multi-epitope vaccine construct against Epstein–Barr virus (EBV)-associated Burkitt lymphoma, with particular emphasis on adjuvant selection, using an immunoinformatics approach. Methods CTL, HTL, and B-cell epitopes derived from gp350, EBNA1, LMP2A, and BZLF1 were screened based on their predicted immunological properties. We assembled candidate epitopes into multi-epitope vaccine constructs with different configurations and evaluated them using immunoinformatics analyses. Results A total of 39 CTL, 21 HTL, and 10 B-cell epitopes met the predefined selection criteria. Five multi-epitope vaccine constructs (R1–R5) were generated and compared. The 50 S ribosomal protein L7/L12 was selected as the adjuvant, and R5 was selected as the final construct based on its comparatively favorable predicted tertiary structure and molecular interactions with TLR2 and TLR4. R5 consisted of 790 amino acids and showed favorable predicted antigenicity (0.6479), solubility, non-allergenicity, and non-toxicity. The predicted population coverage was 98.46% worldwide. Molecular docking demonstrated favorable interactions with TLR2 and TLR4, while iMODS analysis indicated structural stability and favorable dynamic properties.Immune simulation predicted both cellular and humoral immune responses following three administrations at 28-day intervals. Conclusion The L7/L12-adjuvanted R5 construct demonstrated favorable predicted immunological, structural, and molecular characteristics in silico. These findings support its consideration as a candidate for further experimental investigation against EBV-associated Burkitt lymphoma. However, experimental validation is required to determine its actual immunogenicity, safety, and protective efficacy.
Source: Figshare
Institutions: Tanjungpura University, University of Palangka Raya
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223924436