Introduction: Strongyloidiasis, caused by Strongyloides stercoralis, remains a neglected tropical disease (NTD) with significant clinical implications, particularly in immunocompromised individuals. Current serological assays for diagnosing strongyloidiasis are limited by suboptimal sensitivity and specificity. The development of recombinant fusion proteins for serodiagnostic applications represents a promising strategy to improve diagnostic accuracy. This study aimed to design a novel recombinant fusion antigen for the serodiagnosis of strongyloidiasis, using immunoinformatics approaches.
Methods: Four immunogenic proteins (SsIR, L3NieAg.01, Ss3a, and Ss1a) were selected for the design of the fusion antigen. The most immunogenic regions of these proteins were identified based on epitope density and minimal cross-reactivity, and they were linked, using EAAAK linkers. The designed fusion antigen was then evaluated for its physicochemical properties, solubility, antigenicity, and potential cross-reactivity. Its three-dimensional (3D) structure was predicted, and the nucleotide sequence was codon-optimized to ensure efficient expression in Escherichia coli (E. coli). Finally, the optimized sequence was in silico cloned into the pET23a(+) expression vector.
Results: Immunoinformatics analyses demonstrated that the designed fusion antigen exhibits appropriate stability and robust antigenicity while showing no significant cross-reactivity. Codon optimization resulted in a codon adaptation index (CAI) of 0.92, and a GC content adjusted to 47%, confirming its compatibility with the E. coli expression system. Furthermore, no inhibitory cis-regulatory elements or repetitive sequences were identified post-optimization, supporting the feasibility of successful recombinant expression in E. coli.
Conclusion: The bioinformatics findings of this study indicate that the designed fusion antigen holds significant potential for incorporation into ELISA-based serodiagnostic assays for strongyloidiasis.