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Background <p>Cross-reactivity between samples from phylogenetically related organisms remains a significant challenge in antigen detection by lateral flow assays. Recent advancements in bioinformatics tools have significantly enhanced our understanding of the complex interactions between antigenic proteins and antibodies, paving the way for the development of more precise and effective solutions to address these challenges.</p> Methods <p>In this study, we used two common modeling tools, SWISS-MODEL and AlphaFold (2 and 3), to construct three-dimensional (3D) models of the nonstructural 1 (NS1) protein from dengue (DENV) and Zika viruses (ZIKV) based on their protein sequences. Both linear and 3D structures of the proteins were used to predict antigenicity (Jameson-Wolf and Kolaskar-Tongaonkar methods) and B-cell epitopes (Bepipred-2.0, ElliPro, SEPPA3, and DiscoTope methods), and the results obtained from the two modeling tools were compared. Furthermore, molecular simulations were conducted utilizing FORTE and PROCEEDpKa to estimate binding affinities and predict electrostatic epitopes, respectively.</p> Results <p>Our consensus-based pipeline consistently identified two primary antigenic hotspots: 107–127 and 301–319. The antigenicity of the 107–127 region was strongly corroborated by its overlap with the crystallographic binding sites of known antibodies (22NS1, 2B7, and 1G5.3), validating our approach. The 301–319 region emerged as a novel, high-confidence candidate epitope. Furthermore, electrostatic and affinity analyses revealed distinct interaction profiles, providing a molecular basis for discriminating between serotype-specific and cross-reactive antibody responses.</p> Conclusions <p>Our computational pipeline identified key NS1 antigenic determinants, validating the 107–127 region and revealing region 301–319 as a novel epitope candidate. These findings provide specific targets for diagnostics designed to circumvent flavivirus cross-reactivity. The validated workflow itself serves as a robust, adaptable framework for rapid antigen prediction, accelerating the development of targeted diagnostics and immunotherapies for future emerging viruses.</p> Clinical trial number <p>Not applicable.</p>

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Consensus-based computational mapping of NS1 antigenic determinants in dengue and Zika viruses to improve diagnostic specificity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Rafaela S. Valotto,
  • Paulo C. M. Lyra Júnior,
  • Ricardo Machado-de-Ávila,
  • Fabiana V. Campos,
  • Danilo S. Costa,
  • Marco C. C. Guimarães,
  • Fernando L. Barroso da Silva

摘要

Background

Cross-reactivity between samples from phylogenetically related organisms remains a significant challenge in antigen detection by lateral flow assays. Recent advancements in bioinformatics tools have significantly enhanced our understanding of the complex interactions between antigenic proteins and antibodies, paving the way for the development of more precise and effective solutions to address these challenges.

Methods

In this study, we used two common modeling tools, SWISS-MODEL and AlphaFold (2 and 3), to construct three-dimensional (3D) models of the nonstructural 1 (NS1) protein from dengue (DENV) and Zika viruses (ZIKV) based on their protein sequences. Both linear and 3D structures of the proteins were used to predict antigenicity (Jameson-Wolf and Kolaskar-Tongaonkar methods) and B-cell epitopes (Bepipred-2.0, ElliPro, SEPPA3, and DiscoTope methods), and the results obtained from the two modeling tools were compared. Furthermore, molecular simulations were conducted utilizing FORTE and PROCEEDpKa to estimate binding affinities and predict electrostatic epitopes, respectively.

Results

Our consensus-based pipeline consistently identified two primary antigenic hotspots: 107–127 and 301–319. The antigenicity of the 107–127 region was strongly corroborated by its overlap with the crystallographic binding sites of known antibodies (22NS1, 2B7, and 1G5.3), validating our approach. The 301–319 region emerged as a novel, high-confidence candidate epitope. Furthermore, electrostatic and affinity analyses revealed distinct interaction profiles, providing a molecular basis for discriminating between serotype-specific and cross-reactive antibody responses.

Conclusions

Our computational pipeline identified key NS1 antigenic determinants, validating the 107–127 region and revealing region 301–319 as a novel epitope candidate. These findings provide specific targets for diagnostics designed to circumvent flavivirus cross-reactivity. The validated workflow itself serves as a robust, adaptable framework for rapid antigen prediction, accelerating the development of targeted diagnostics and immunotherapies for future emerging viruses.

Clinical trial number

Not applicable.