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<p>Marburg virus (MARV) is classified as a risk group 4 pathogen by the WHO due to its high fatality rates, frequent person-to-person transmission, and lack of approved vaccines or treatments. This highlights the need for a universally effective MARV vaccine. In this study, we employed computational bioinformatics methods to analyze conserved sequences of the VP30 transcriptional activator, using databases and bioinformatics tools. Amino acid sequences were sourced from NCBI, and antigenicity was assessed using Kolaskar, Tongaonkar, and VaxiJen servers. B and T cell epitopes were identified using ABCPred and the Immune Epitope Database, providing insights into potential immunogenic regions. The VP30 protein, crucial in both physiological and pathological processes, emerged as a promising target for vaccine development. Key epitopes from VP30, including IGLPCTDGL and PCKIGLPCTIGLPCTD, showed efficacy as T and B cell epitopes. We designed a multi-epitope vaccine incorporating these epitopes, demonstrating favorable physicochemical and immunological properties. Molecular dynamics simulations confirmed that both mono- and multi-epitopes improve the vaccine’s therapeutic potential. Our analysis suggests the proposed vaccine candidate could trigger an immune response against MARV. However, experimental validation is needed to confirm its immunomodulatory properties and effectiveness.</p>

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VP30 vanguard: pioneering an in-silico multi-epitope vaccine against Marburg virus

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Saba Beigh,
  • Mohit Sharma,
  • Inderjeet Bhogal,
  • Arshad Jawed,
  • Mohtashim Lohani,
  • Sajad Ahmad Dar

摘要

Marburg virus (MARV) is classified as a risk group 4 pathogen by the WHO due to its high fatality rates, frequent person-to-person transmission, and lack of approved vaccines or treatments. This highlights the need for a universally effective MARV vaccine. In this study, we employed computational bioinformatics methods to analyze conserved sequences of the VP30 transcriptional activator, using databases and bioinformatics tools. Amino acid sequences were sourced from NCBI, and antigenicity was assessed using Kolaskar, Tongaonkar, and VaxiJen servers. B and T cell epitopes were identified using ABCPred and the Immune Epitope Database, providing insights into potential immunogenic regions. The VP30 protein, crucial in both physiological and pathological processes, emerged as a promising target for vaccine development. Key epitopes from VP30, including IGLPCTDGL and PCKIGLPCTIGLPCTD, showed efficacy as T and B cell epitopes. We designed a multi-epitope vaccine incorporating these epitopes, demonstrating favorable physicochemical and immunological properties. Molecular dynamics simulations confirmed that both mono- and multi-epitopes improve the vaccine’s therapeutic potential. Our analysis suggests the proposed vaccine candidate could trigger an immune response against MARV. However, experimental validation is needed to confirm its immunomodulatory properties and effectiveness.