A Machine Learning-Guided Approach for a Multi-Epitope HIV Vaccine Design
DOI:
https://doi.org/10.5753/reic.2025.6062Keywords:
HIV vaccine, multi-epitope design, machine learning, immunoinformatics, chimeric proteinAbstract
Developing an effective HIV vaccine remains challenging due to the virus's variability and complex immune responses. We propose a novel multi-epitope vaccine design using machine learning and computational methods to identify conserved, immunodominant epitopes from diverse HIV variants. These epitopes, selected to elicit humoral and cellular responses -- targeting CD4+ T cells, CD8+ cytotoxic T cells, and B cells -- are incorporated into a chimeric protein delivered via a viral vector to enhance immunity. Our framework integrates epitope selection, in silico physicochemical predictions, 3D construction of the chimeric protein, illustrated in Figure 1, and in vitro analysis, contributing to the development of a broadly protective and durable HIV vaccine.
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Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., and Sayers, E. W. (2012). GenBank. Nucleic Acids Research, 41(D1):D36–D42. DOI: 10.1093/nar/gks1195.
Chakraborty, S., Askari, M., Barai, R. S., and Idicula-Thomas, S. (2024). PBITv3: A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates. Protein Science, 33(2):e4892. DOI: 10.1002/pro.4892.
Cohen, Y. and Dolin, R. (2013). Novel HIV vaccine strategies: overview and perspective. Ther Adv Vaccines, 1(3):99–112. DOI: 10.1177/2051013613494535.
Dimitrov, I., Bangov, I., Flower, D. R., and Doytchinova, I. (2014). AllerTOP v. 2—a server for in silico prediction of allergens. Journal of Molecular Modeling, 20:1–6. DOI: 10.1007/s00894-014-2278-5.
Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Consortium, O. S. D. D., and Raghava, G. P. (2013). In silico approach for predicting toxicity of peptides and proteins. PLoS One, 8(9):e73957. DOI: 10.1371/journal.pone.0073957.
Gómez, C. E., Perdiguero, B., García-Arriaza, J., and Esteban, M. (2012). Poxvirus vectors as HIV/AIDS vaccines in humans. Human Vaccines & Immunotherapeutics, 8(9):1192–1207. DOI: 10.4161/hv.20778.
Hebditch, M., Carballo-Amador, M. A., Charonis, S., Curtis, R., and Warwicker, J. (2017). Protein–Sol: A web tool for predicting protein solubility from sequence. Bioinformatics, 33(19):3098–3100. DOI: 10.1093/bioinformatics/btx345.
Hojo-Souza, N. S., de Castro, J. T., Rivelli, G. G., Azevedo, P. O., Oliveira, E. R., Faustino, L. P., Salazar, N., Bagno, F. F., Carvalho, A. F., Rattis, B., et al. (2024). SPIN-Tec: A T cell-based recombinant vaccine that is safe, immunogenic, and shows high efficacy in experimental models challenged with SARS-CoV-2 variants of concern. Vaccine, 42(26):126394. DOI: 10.1016/j.vaccine.2024.126394.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873):583–589. DOI: 10.1038/s41586-021-03819-2.
Katoh, K. and Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Molecular Biology and Evolution, 30(4):772–780. DOI: 10.1093/molbev/mst010.
Kaur, A. and Vaccari, M. (2024). Exploring HIV vaccine progress in the pre-clinical and clinical setting: From history to future prospects. Viruses, 16(3):368. DOI: 10.3390/v16030368.
Mu, Z., Haynes, B. F., and Cain, D. W. (2021). HIV mRNA vaccines—progress and future paths. Vaccines, 9(2). DOI: 10.3390/vaccines9020134.
Ng’uni, T., Chasara, C., and Ndhlovu, Z. M. (2020). Major scientific hurdles in HIV vaccine development: Historical perspective and future directions. Frontiers in Immunology, 11:590780. DOI: 10.3389/fimmu.2020.590780.
Shen, J., Liu, F., Tu, Y., and Tang, C. (2021). Finding gene network topologies for given biological function with recurrent neural network. Nature Communications, 12(1):3125. DOI: 10.1038/s41467-021-23420-5.
Vita, R., Blazeska, N., Marrama, D., Members, I. C. T., Duesing, S., Bennett, J., Greenbaum, J., De Almeida Mendes, M., Mahita, J., Wheeler, D. K., Cantrell, J. R., Overton, J. A., Natale, D. A., Sette, A., and Peters, B. (2024). The Immune Epitope Database (IEDB): 2024 update. Nucleic Acids Research, 53(D1):D436–D443. DOI: 10.1093/nar/gkae1092.
Zhang, L. (2018). Multi-epitope vaccines: A promising strategy against tumors and viral infections. Cellular & Molecular Immunology, 15(2):182–184. DOI: 10.1038/cmi.2017.92.
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