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Identification of Immunodominant Epitopes of Dengue Virus 2 Envelope and NS1 Proteins: Evaluating the Diagnostic Potential of a Synthetic Peptide

Overview
Journal Mol Diagn Ther
Date 2024 Jul 9
PMID 38980575
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Abstract

Background And Objective: Dengue is a major infectious disease with potential for outbreaks and epidemics. A specific and sensitive diagnosis is a prerequisite for clinical management of the disease. We designed our study to identify epitopes on the Dengue virus (DENV) envelope (E) and non-structural protein 1 (NS1) with potential for diagnosis.

Methods: Serology and immunoinformatic approaches were employed. We collected DENV-positive, DENV-negative and Japanese encephalitis virus-positive samples from collaborating hospitals in 2019 and 2022-2023. Seropositive peptides in 15-18 mer peptide arrays of E and NS1 proteins of DENV2 were determined by an indirect enzyme-linked immunosorbent assay. B-cell linear and conformational epitopes were predicted using BepiPred2.0 and ElliPro, respectively. A consensus recombinant peptide was designed, synthesised and evaluated for its diagnostic potential using patient sera.

Results: Eight peptides of E protein and six peptides of NS1 protein were identified to be the most frequently recognised by Dengue-positive patients. These peptide sequences were compared with B-cell epitope regions and found to be overlapped with predicted B-cell linear and conformational epitopes. EP11 and NSP15 showed a 100% amino acid sequence overlap with B-cell epitopes. EP1 and NSP15 had 14 whereas EP28, EP31, EP60 16, NSP12 and NSP32 had more than 15 interacting interface residues with a neutralising antibody, suggesting a strength of interaction. Interestingly, potential epitopes identified were localised on the surface of proteins as visualised by PyMOL. Validation with a recombined synthetic peptide yielded 92.3% sensitivity and 91.42% specificity.

Conclusions: Immunodominant regions identified by serology and computationally predicted epitopes overlapped, thereby showing the robustness of the methodology and the peptide designed for diagnosis.

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