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<p><i>Babesia microti</i> is an emerging tick-transmitted agent causing babesiosis in humans, becoming a major concern to public health due to the lack of a vaccine. <i>In silico</i> methods such as reverse vaccinology and immunoinformatics offer a convenient tool for identifying promising vaccine targets. Using computational immunoinformatics and molecular modelling tools, the current study aims to develop a multi-epitope vaccination against <i>B. microti</i>. For identifying the antigenic proteins, subtractive proteomics, NCBI, and PPGA were used, whereas for their screening, ProtParam (ExPASy), VaxiJen 2.0, AllerTOP v2.0, and BLASTp were utilized. For B-cell epitope prediction, IEDB (BepiPred 2.0) was used, and for T-cell epitope prediction, NetMHCpan 4.1 was used, along with other characteristics such as antigenicity, allergenicity, toxicity, and solubility, which were predicted later. A multi-epitope vaccine construct was designed using the selected epitopes linked together with the help of EAAAK and GPGPG linkers, with β-defensin as an adjuvant. For structural modeling and refinement, Scratch Protein Predictor and GalaxyRefine were used, followed by validation using PROCHECK. Further, molecular docking was done using ClusPro, while molecular dynamics simulations were done using AMBER 22. In addition, immune simulation and cloning were conducted. From a total of 3601 proteins, four non-homologous and non-allergenic proteins were selected for use in the design process, from which a total of seven epitopes were selected (MHC class I − 4; MHC class II − 3). Structural analysis of the designed vaccine revealed good stability, which further increased when the vaccine was modified using disulfide bonds. Population coverage revealed that the vaccine had good global coverage (99.74%). Docking revealed good binding of the designed vaccine with the toll-like receptors TLR-2 and TLR-4. Analysis of molecular dynamics and binding energies of TLR-2 (-135.11&#xa0;kcal/mol), and TLR-4 (-157.27&#xa0;kcal/mol) receptors further supported the stability of complexes. In summary, the <i>in silico</i> and biophysical studies show that the designed multi-epitope vaccine for <i>B. microti</i> may have desirable stability, immunogenicity, and positive interaction with TLR-2 and TLR-4 receptors. Nevertheless, experimental validation is needed to establish its safety and effectiveness.</p>

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Computational immunoinformatics and molecular modeling approaches for designing a multi-epitope vaccine against Babesia microti

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Muharib Alruwaili,
  • Intisar Alruwaili,
  • Yasir Alruwaili,
  • Hasan Ejaz,
  • Bi Bi Zainab Mazhari,
  • Muhammad Tahir ul Qamar,
  • Muhammad Umer Khan

摘要

Babesia microti is an emerging tick-transmitted agent causing babesiosis in humans, becoming a major concern to public health due to the lack of a vaccine. In silico methods such as reverse vaccinology and immunoinformatics offer a convenient tool for identifying promising vaccine targets. Using computational immunoinformatics and molecular modelling tools, the current study aims to develop a multi-epitope vaccination against B. microti. For identifying the antigenic proteins, subtractive proteomics, NCBI, and PPGA were used, whereas for their screening, ProtParam (ExPASy), VaxiJen 2.0, AllerTOP v2.0, and BLASTp were utilized. For B-cell epitope prediction, IEDB (BepiPred 2.0) was used, and for T-cell epitope prediction, NetMHCpan 4.1 was used, along with other characteristics such as antigenicity, allergenicity, toxicity, and solubility, which were predicted later. A multi-epitope vaccine construct was designed using the selected epitopes linked together with the help of EAAAK and GPGPG linkers, with β-defensin as an adjuvant. For structural modeling and refinement, Scratch Protein Predictor and GalaxyRefine were used, followed by validation using PROCHECK. Further, molecular docking was done using ClusPro, while molecular dynamics simulations were done using AMBER 22. In addition, immune simulation and cloning were conducted. From a total of 3601 proteins, four non-homologous and non-allergenic proteins were selected for use in the design process, from which a total of seven epitopes were selected (MHC class I − 4; MHC class II − 3). Structural analysis of the designed vaccine revealed good stability, which further increased when the vaccine was modified using disulfide bonds. Population coverage revealed that the vaccine had good global coverage (99.74%). Docking revealed good binding of the designed vaccine with the toll-like receptors TLR-2 and TLR-4. Analysis of molecular dynamics and binding energies of TLR-2 (-135.11 kcal/mol), and TLR-4 (-157.27 kcal/mol) receptors further supported the stability of complexes. In summary, the in silico and biophysical studies show that the designed multi-epitope vaccine for B. microti may have desirable stability, immunogenicity, and positive interaction with TLR-2 and TLR-4 receptors. Nevertheless, experimental validation is needed to establish its safety and effectiveness.