DESAIN VAKSIN MULTI-EPITOP TERHADAP VIRUS MARBURG (MARV) BERBASIS PROTEIN VP35 MENGGUNAKAN PENDEKATAN IMMUNOINFORMATIKA: IN-SILICO
DOI:
https://doi.org/10.36423/pharmacoscript.v8i2.2184Keywords:
Virus Marburg, Immunoinformatika, Bioinformatika, VP35, Desain VaksinAbstract
Virus Marburg (MARV) merupakan salah satu virus zoonosis dari keluarga Filoviridae dengan tingkat kematian hingga 88%. Pertumbuhan populasi global yang pesat meningkatkan risiko paparan penyakit akibat perubahan iklim dan ekologi. Untuk pencegahan, vaksin menjadi upaya terbaik. Penelitian ini bertujuan merancang vaksin berbasis peptida dari protein VP35 dan mengevaluasi interaksinya dengan sistem imun (TLR3, MHC-I, dan MHC-II) berdasarkan parameter stabilitas interaksi molekuler seperti skor ikatan, nilai elektrostatik, dan desolvasi. Metode yang digunakan adalah pendekatan immunoinformatics secara in silico, meliputi pemilihan protein target, prediksi antigenisitas, prediksi epitop T CD8+, prediksi epitop T CD4+, prediksi epitop B, konstruksi vaksin & visualisasi 3D, validasi struktur vaksin, analisis docking dan prediksi cakupan populasi. Hasil menunjukkan bahwa desain vaksin memiliki interaksi yang stabil terhadap TLR3, MHC-I (HLA-A*11:01), dan MHC-II (HLA-DR1), dengan nilai skor total berturut-turut sebesar –43,698, –42,192, dan –51,899, menunjukkan afinitas ikatan yang kuat secara in silico. Temuan ini mengindikasikan potensi desain vaksin berbasis epitop VP35 sebagai kandidat imunogenik terhadap MARV, meskipun perlu dioptimalkan lebih lanjut mengingat cakupan populasi yang masih terbatas.
References
Abir, M. H., Rahman, T., Das, A., Etu, S. N., Nafiz, I. H., Rakib, A., Mitra, S., Emran, T. Bin, Dhama, K., Islam, A., Siyadatpanah, A., Mahmud, S., Kim, B., & Hassan, M. M. (2022). Pathogenicity and virulence of Marburg virus. Virulence, 13(1), 609–633. https://doi.org/10.1080/21505594.2022.2054760
Bibi, S., Ullah, I., Zhu, B., Adnan, M., Liaqat, R., Kong, W. B., & Niu, S. (2021). In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Scientific Reports, 11(1), 1–16. https://doi.org/10.1038/s41598-020-80899-6
Firmansyah, M. A., Susilo, A., Haryanti, S., & Herowati, R. (2021). Desain Vaksin Berbasis Epitop dengan Pendekatan Bioinformatika untuk Menekan Glikoprotein Spike SARS-CoV-2. Jurnal Farmasi Indonesia, 18, 82–96. https://doi.org/10.31001/jfi.v18i2.1351
Kar, T., Narsaria, U., Basak, S., Deb, D., Castiglione, F., Mueller, D. M., & Srivastava, A. P. (2020). A candidate multi-epitope vaccine against SARS-CoV-2. Scientific Reports, 10(1), 1–24. https://doi.org/10.1038/s41598-020-67749-1
Khairkhah, N., Aghasadeghi, M. R., Namvar, A., & Bolhassani, A. (2020). Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis. PLoS ONE, 15(10 October), 1–28. https://doi.org/10.1371/journal.pone.0240577
Khasana, A. S. N., Hartono, N. L. S., Permatasari, V. O., Ramadhan, D. L., & Sumadi, F. A. N. (2023). in Silico Design of B-Cell Epitope Based Peptide Vaccine for Varicella Zoster Virus. Indonesian Journal of Biotechnology and Biodiversity, 7(1), 1–11. https://doi.org/10.47007/ijobb.v7i1.157
Mahmud, S. M. N., Rahman, M., Kar, A., Jahan, N., & Khan, A. (2019). Designing of an Epitope- Based Universal Peptide Vaccine against Highly Conserved Regions in RNA Dependent RNA Polymerase Protein of Human Marburg Virus: A Computational Assay. Anti-Infective Agents, 18(3), 294–305. https://doi.org/10.2174/2211352517666190717143949
Naveed, M., Tehreem, S., Arshad, S., Bukhari, S. A., Shabbir, M. A., Essa, R., Ali, N., Zaib, S., Khan, A., Al-Harrasi, A., & Khan, I. (2021). Design of a novel multiple epitope-based vaccine: An immunoinformatics approach to combat SARS-CoV-2 strains. Journal of Infection and Public Health, 14(7), 938–946. https://doi.org/10.1016/j.jiph.2021.04.010
NetCTL. (2023). NetCTL-1.2 Predection of CTL epitopes in protein sequences. DTU Health Tech. https://services.healthtech.dtu.dk/services/NetCTL-1.2/
Ong, E., He, Y., & Yang, Z. (2020). Epitope promiscuity and population coverage of Mycobacterium tuberculosis protein antigens in current subunit vaccines under development. Infection, Genetics and Evolution, 80(January), 104186. https://doi.org/10.1016/j.meegid.2020.104186
Rawal, K., Sinha, R., Abbasi, B. A., Chaudhary, A., Nath, S. K., Kumari, P., Preeti, P., Saraf, D., Singh, S., Mishra, K., Gupta, P., Mishra, A., Sharma, T., Gupta, S., Singh, P., Sood, S., Subramani, P., Dubey, A. K., Strych, U., … Bottazzi, M. E. (2021). Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Scientific Reports, 11(1), 1–25. https://doi.org/10.1038/s41598-021-96863-x
Renadi, S., Pratita, A. T. K., Mardianingrum, R., & Ruswanto, dan R. (2023). The Potency of Alkaloid Derivates as Anti-Breast Cancer Candidates: In Silico Study. Jurnal Kimia Valensi, 9(1), 89–108. https://doi.org/10.15408/jkv.v9i1.31481
Rezaldi, F., Taupiqurrohman, O., Fadillah, M. F., Rochmat, A., Humaedi, A., & Fadhilah, F. (2021). Identifikasi Kandidat Vaksin COVID-19 Berbasis Peptida dari Glikoprotein Spike SARS CoV-2 untuk Ras Asia secara In Silico. Jurnal Biotek Medisiana Indonesia , 10(1), 77–85. https://ejournal2.litbang.kemkes.go.id/index.php/jbmi/article/view/5031/2299
Ruswanto, R., Mardianingrum, R., Nofianti, T., Fizriani, R., & Siswandono, S. (2023). Computational Study of Bis-(1-(Benzoyl)-3-Methyl Thiourea) Platinum (II) Complex Derivatives as Anticancer Candidates. Advances and Applications in Bioinformatics and Chemistry, 16(January), 15–36. https://doi.org/10.2147/AABC.S392068.
Sakabe, S., Sullivan, B. M., Hartnett, J. N., Robles-Sikisaka, R., Gangavarapu, K., Cubitt, B., ... & Oldstone, M. B. (2018). Analysis of CD8+ T cell response during the 2013–2016 Ebola epidemic in West Africa. Proceedings of the National Academy of Sciences, 115(32), E7578-E7586.
Sanami, S., Alizadeh, M., Nosrati, M., Dehkordi, K. A., Azadegan-Dehkordi, F., Tahmasebian, S., Nosrati, H., Arjmand, M. H., Ghasemi-Dehnoo, M., Rafiei, A., & Bagheri, N. (2021). Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study. Computers in Biology and Medicine, 133(January), 104390. https://doi.org/10.1016/j.compbiomed.2021.104390
Syakuran, L. "Abdan. (2020). Desain Kandidat Vaksin SARS-CoV-2 Menggunakan Pendekatan Imunoinformatika. ResearchGate, 1–11. https://doi.org/10.13140/80 2.2.33453.31202.
Tahir ul Qamar, M., Rehman, A., Tusleem, K., Ashfaq, U. A., Qasim, M., Zhu, X., Fatima, I., Shahid, F., & Chen, L. L. (2020). Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. PLoS ONE, 15(12 December 2020), 1–25. https://doi.org/10.1371/journal.pone.0244176
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Asopari Ilham Nurfadilah, Pratita Anindita Tri Kusuma , Mardianingrum Richa, Ruswanto Ruswanto

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication, with the work [SPECIFY PERIOD OF TIME] after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).