Cystic echinococcosis (CE), caused by Echinococcus granulosus, is a zoonotic disease with substantial health and economic consequences. Limitations of current serological assays, including variable sensitivity and cross-reactivity, support the development of improved recombinant diagnostic antigens. In this study, a multi-epitope chimeric antigen was designed using an immunoinformatics pipeline. Twelve linear B-cell epitopes were selected from three literature-derived antigens and one proteome-mined candidate based on predicted antigenicity, non-allergenicity, non-toxicity, and the absence of significant similarity to human, canine, and retained Taenia protein matches at the applied E-value threshold. The epitopes were joined using EAAAK linkers and combined with solubility and affinity tags. The final 380- amino-acid construct showed predicted antigenicity (VaxiJen score, 0.6848), a molecular weight of 41.65 kDa, an instability index of 36.22, and favorable predicted solubility. AlphaFold2 generated a low-confidence global model (pLDDT = 52.5; pTM = 0.361), consistent with the flexible and linker-rich architecture of the artificial construct. Accordingly, the structural model was interpreted qualitatively rather than as evidence of a unique native-like fold. The proposed construct is a computational candidate for CE serodiagnosis; however, recombinant-expression and validation with well-characterized CE, disease-control, and healthy-control serum panels are required before diagnostic performance can be established.
