In Silico Prediction and Structural Mapping of Linear B-Cell Epitopes in Andes Virus Glycoprotein as Potential Vaccine Candidates
DOI:
https://doi.org/10.22452/jtoh.vol1.7Keywords:
Andes virus, B-cell epitope, glycoprotein, immunoinformatics, structural mapping, vaccine candidateAbstract
Andes virus (ANDV), a pathogenic orthohantavirus associated with hantavirus cardiopulmonary syndrome, remains a public health concern because of its severe clinical outcomes and the absence of broadly approved vaccines for human use. The viral glycoprotein is a relevant antigenic target because of its surface localization and role in viral entry. This study aimed to identify and structurally map linear B-cell epitope candidates in the Andes virus glycoprotein using an immunoinformatics-based approach. The ANDV glycoprotein sequence was retrieved from UniProt, and linear B-cell epitopes were predicted using BepiPred 3.0 with a threshold of 0.030. Candidate peptides were further evaluated for antigenicity, allergenicity, and toxicity using VaxiJen v2.0, AllerTOP v2.1, and ToxinPred, respectively. The three-dimensional glycoprotein model was generated using AlphaFold, refined with GalaxyWEB, and validated using ERRAT and Ramachandran plot analysis. Five preliminary epitope candidates were identified, among which E1, E2, E3, and E4 met the antigenicity criterion. The refined structure showed favorable validation parameters, with an ERRAT score of 97.73 and 97.88% of residues in the favored Ramachandran region. Structural mapping indicated that E1 (EWRKKSDTTDTTNAASTTFE) and E2 (QTKTVNLRGT) were positioned on exposed surface regions, supporting their prioritization as the most promising candidates. E4 (PSTSSTDT) also showed potential, whereas E3 (HPRGEDHD), despite having the highest antigenicity score, appeared less structurally exposed. These findings provide preliminary computational evidence for surface-accessible linear B-cell epitope candidates in ANDV glycoprotein. However, experimental validation is required to confirm their immunological relevance.
References
Abdulla, F., Nain, Z., Hossain, Md. M., Syed, S. B., Ahmed Khan, M. S., & Adhikari, U. K. (2021). A comprehensive screening of the whole proteome of hantavirus and designing a multi-epitope subunit vaccine for cross-protection against hantavirus: Structural vaccinology and immunoinformatics study. Microbial Pathogenesis, 150, 104705. https://doi.org/10.1016/j.micpath.2020.104705
Afnani, M. R., Emilia, N. F., Nurlaili, E., Rovik, A., Ansori, A. Nm., Parikesit, A. A., Chatterjee, S., Mahata, J., Firdous, A., Sahadewa, S., Khairullah, A. R., Adnyana, I. Mdm., Rebezov, M., Abdurasulov, A., Durry, F. D., & Rollando, R. (2026). In silico design of a multi-epitope rabies vaccine candidate incorporating African HLA diversity: A reverse vaccinology approach. Narra X, 4(1), e263. https://doi.org/10.52225/narrax.v4i1.263
Afnani, M. R., & Purnama, E. R. (2025). Design of a Recombinant Multi-Epitope Subunit Vaccine Targeting Glycoproteins of Herpes Simplex Virus Type 2 Using an Immunoinformatics Approach. Bioactivities, 3(1), 55–71. https://doi.org/10.47352/bioactivities.2963-654X.280
Afnani, Moh. R., Kharisma, V. D., Khairullah, A. R., Abdurasulov, A., Abdyldayeva, R., Rebezov, M., Parikesit, A. A., Jakhmola, V., Rollando, R., & Ansori, A. N. M. (2026). Multi-epitope vaccine design against Nipah virus: An immunoinformatics approach targeting South and Southeast Asia populations. In Silico Pharmacology, 14(2), 133. https://doi.org/10.1007/s40203-026-00607-6
Agarwal, V., & McShan, A. C. (2024). The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nature Chemical Biology, 20(8), 950–959. https://doi.org/10.1038/s41589-024-01638-w
Angulo, J., Martínez-Valdebenito, C., Marco, C., Galeno, H., Villagra, E., Vera, L., Lagos, N., Becerra, N., Mora, J., Bermúdez, A., Díaz, J., Ferrés, M., & López-Lastra, M. (2017). Serum levels of interleukin-6 are linked to the severity of the disease caused by Andes Virus. PLOS Neglected Tropical Diseases, 11(7), e0005757. https://doi.org/10.1371/journal.pntd.0005757
Banerjee, N., & Mukhopadhyay, S. (2016). Viral glycoproteins: Biological role and application in diagnosis. VirusDisease, 27(1), 1–11. https://doi.org/10.1007/s13337-015-0293-5
Barrera, A., Ramos, H., Fuentes-Luppichini, E., Martínez-Valdebenito, C., Pradenas, J., Rogers, C., Palma, C., Campillo, C., Navarrete, M., Corre, N. L., Angulo, J., & Ferrés, M. (2025). New Andes virus isolate haplotype obtained during prospective close contacts follow-up of an Hantavirus cardiopulmonary syndrome fatal case, Chile. Current Research in Microbial Sciences, 9, 100472. https://doi.org/10.1016/j.crmicr.2025.100472
Beltrán-Ortiz, C. E., Starck-Mendez, M. F., Fernández, Y., Farnós, O., González, E. E., Rivas, C. I., Camacho, F., Zuñiga, F. A., Toledo, J. R., & Sánchez, O. (2017). Expression and purification of the surface proteins from Andes virus. Protein Expression and Purification, 139, 63–70. https://doi.org/10.1016/j.pep.2015.09.013
Caoili, S. E. C. (2022). Comprehending B-Cell Epitope Prediction to Develop Vaccines and Immunodiagnostics. Frontiers in Immunology, 13, 908459. https://doi.org/10.3389/fimmu.2022.908459
Chediack, V., Calvo, M., Cunto, E., Santos, A., Rodríguez, A., & Suberviola, B. (2026). Hantavirus infection: A narrative review focusing on epidemiology, diagnosis, infection control and treatment in the era of globalisation. Medicina Intensiva (English Edition), 502523. https://doi.org/10.1016/j.medine.2026.502523
Choi, S., & Kim, D. (2024). B cell epitope prediction by capturing spatial clustering property of the epitopes using graph attention network. Scientific Reports, 14(1), 27496. https://doi.org/10.1038/s41598-024-78506-z
Cia, G., Pucci, F., & Rooman, M. (2023). Critical review of conformational B-cell epitope prediction methods. Briefings in Bioinformatics, 24(1), bbac567. https://doi.org/10.1093/bib/bbac567
Cifuentes-Muñoz, N., Salazar-Quiroz, N., & Tischler, N. (2014). Hantavirus Gn and Gc Envelope Glycoproteins: Key Structural Units for Virus Cell Entry and Virus Assembly. Viruses, 6(4), 1801–1822. https://doi.org/10.3390/v6041801
Clifford, J. N., Høie, M. H., Deleuran, S., Peters, B., Nielsen, M., & Marcatili, P. (2022). BepiPred‐3.0: Improved B‐cell epitope prediction using protein language models. Protein Science, 31(12). https://doi.org/10.1002/pro.4497
Colovos, C., & Yeates, T. O. (1993). Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Science, 2(9), 1511–1519. https://doi.org/10.1002/pro.5560020916
Dimitrov, I., Bangov, I., Flower, D. R., & Doytchinova, I. (2014). AllerTOP v.2—A server for in silico prediction of allergens. Journal of Molecular Modeling, 20(6), 2278. https://doi.org/10.1007/s00894-014-2278-5
Doytchinova, I. A., & Flower, D. R. (2007). VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics, 8(1), 4. https://doi.org/10.1186/1471-2105-8-4
Francino-Urdaniz, I. M., & Whitehead, T. A. (2021). An overview of methods for the structural and functional mapping of epitopes recognized by anti-SARS-CoV-2 antibodies. RSC Chemical Biology, 2(6), 1580–1589. https://doi.org/10.1039/D1CB00169H
Galanis, K. A., Nastou, K. C., Papandreou, N. C., Petichakis, G. N., Pigis, D. G., & Iconomidou, V. A. (2021). Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface. International Journal of Molecular Sciences, 22(6), 3210. https://doi.org/10.3390/ijms22063210
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P. (2019). Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes. Frontiers in Immunology, 10, 298. https://doi.org/10.3389/fimmu.2019.00298
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
Ko, J., Park, H., Heo, L., & Seok, C. (2012). GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Research, 40(W1), W294–W297. https://doi.org/10.1093/nar/gks493
Koopmans, M., Brechot, C., Bruzzone, R., Diagne, M. M., Drosten, C., Eckerle, I., Gallo, R., Hayman, D. T. S., Horby, P., Hotez, P., Jong, M. de, Krammer, F., Manuguerra, J. C., Palacios, G., Palmarini, M., Schreijer, A., Tischler, N. D., Oliveira, T. de, Munnink, B. B. O., … Karlsson, E. A. (2026). Andes Virus on a Cruise Ship, what it Tells us About the Global Pandemic Preparedness Agenda. The Lancet Regional Health – Europe, 0(0). https://doi.org/10.1016/j.lanepe.2026.101755
Kovalevskiy, O., Mateos-Garcia, J., & Tunyasuvunakool, K. (2024). AlphaFold two years on: Validation and impact. Proceedings of the National Academy of Sciences, 121(34), e2315002121. https://doi.org/10.1073/pnas.2315002121
Laskowski, R. A., Furnham, N., & Thornton, J. M. (2013). The Ramachandran Plot And Protein Structure Validation. In Biomolecular Forms and Functions (pp. 62–75). WORLD SCIENTIFIC / INDIAN INST OF SCIENCE, INDIA. https://doi.org/10.1142/9789814449144_0005
Martinez-Valdebenito, C., Calvo, M., Vial, C., Mansilla, R., Marco, C., Palma, R. E., Vial, P. A., Valdivieso, F., Mertz, G., & Ferrés, M. (2014). Person-to-Person Household and Nosocomial Transmission of Andes Hantavirus, Southern Chile, 2011. Emerging Infectious Diseases, 20(10), 1637–1644. https://doi.org/10.3201/eid2010.140353
Moirano, G., Botta, A., Yang, M., Mangeruga, M., Murray, K., & Vineis, P. (2024). Land-cover, land-use and human hantavirus infection risk: A systematic review. Pathogens and Global Health, 118(5), 361–375. https://doi.org/10.1080/20477724.2023.2272097
Potocnakova, L., Bhide, M., & Pulzova, L. B. (2016). An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction. Journal of Immunology Research, 2016, 1–11. https://doi.org/10.1155/2016/6760830
Quarleri, J., Jarmoluk, P., Mazzitelli, I., & Delpino, M. V. (2026). Andes virus and the aging host: Lessons from the 2026 outbreak and a framework for age-related disease severity. GeroScience. https://doi.org/10.1007/s11357-026-02352-2
Ramaraj, T., Angel, T., Dratz, E. A., Jesaitis, A. J., & Mumey, B. (2012). Antigen–antibody interface properties: Composition, residue interactions, and features of 53 non-redundant structures. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, 1824(3), 520–532. https://doi.org/10.1016/j.bbapap.2011.12.007
Rathore, A. S., Choudhury, S., Arora, A., Tijare, P., & Raghava, G. P. S. (2024). ToxinPred 3.0: An improved method for predicting the toxicity of peptides. Computers in Biology and Medicine, 179, 108926. https://doi.org/10.1016/j.compbiomed.2024.108926
Reis, P. B. P. S., Barletta, G. P., Gagliardi, L., Fortuna, S., Soler, M. A., & Rocchia, W. (2022). Antibody-Antigen Binding Interface Analysis in the Big Data Era. Frontiers in Molecular Biosciences, 9, 945808. https://doi.org/10.3389/fmolb.2022.945808
Riswana, F. R., & Punjungsari, T. N. (2025). Analysis of B-Cell Epitope on E8l Protein Monkeypox Virus as a Peptide-Based Vaccine Candidate. BIO Web of Conferences, 198, 07001. https://doi.org/10.1051/bioconf/202519807001
Sanchez-Trincado, J. L., Gomez-Perosanz, M., & Reche, P. A. (2017). Fundamentals and Methods for T- and B-Cell Epitope Prediction. Journal of Immunology Research, 2017, 1–14. https://doi.org/10.1155/2017/2680160
Shimizu, K., Yoshimatsu, K., Koma, T., Yasuda, S. P., & Arikawa, J. (2013). Role of nucleocapsid protein of hantaviruses in intracellular traffic of viral glycoproteins. Virus Research, 178(2), 349–356. https://doi.org/10.1016/j.virusres.2013.09.022
Terwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., & Adams, P. D. (2024). AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nature Methods, 21(1), 110–116. https://doi.org/10.1038/s41592-023-02087-4
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., Žídek, A., Green, T., Tunyasuvunakool, K., Petersen, S., Jumper, J., Clancy, E., Green, R., Vora, A., Lutfi, M., … Velankar, S. (2022). AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, 50(D1), D439–D444. https://doi.org/10.1093/nar/gkab1061
Villanueva-Flores, F., Sanchez-Villamil, J. I., & Garcia-Atutxa, I. (2025). AI-driven epitope prediction: A systematic review, comparative analysis, and practical guide for vaccine development. Npj Vaccines, 10(1), 207. https://doi.org/10.1038/s41541-025-01258-y
Warner, B. M., Prévost, J., Tailor, N., Deschambault, Y., Sloan, A., Allarie, J., Klassen, L., Frost, K., Booth, S., Audet, J., Soule, G., & Safronetz, D. (2025). High genomic stability of Andes virus following successive passage in vivo in Syrian hamsters. Journal of Virology, 99(8), e00512-25. https://doi.org/10.1128/jvi.00512-25
Yao, B., Zheng, D., Liang, S., & Zhang, C. (2013). Conformational B-Cell Epitope Prediction on Antigen Protein Structures: A Review of Current Algorithms and Comparison with Common Binding Site Prediction Methods. PLoS ONE, 8(4), e62249. https://doi.org/10.1371/journal.pone.0062249
Zeng, X., Bai, G., Sun, C., & Ma, B. (2023). Recent Progress in Antibody Epitope Prediction. Antibodies, 12(3), 52. https://doi.org/10.3390/antib12030052
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