Development and use of machine learning algorithms in vaccine target selection | npj Vaccines – Nature.com
January 20, 2024
He, L. & Zhu, J. Computational tools for epitope vaccine design and evaluation. Curr. Opin. Virol. 11, 103112 (2015).
Article CAS PubMed PubMed Central Google Scholar
Sette, A. & Rappuoli, R. Reverse vaccinology: developing vaccines in the era of genomics. Immunity 33, 530541 (2010).
Article CAS PubMed PubMed Central Google Scholar
Kyriakidis, N. C. et al. SARS-CoV-2 vaccines strategies: a comprehensive review of phase 3 candidates. npj Vaccines 6, 117 (2021).
Soria-Guerra, R. E., Nieto-Gomez, R., Govea-Alonso, D. O. & Rosales-Mendoza, S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J. Biomed. Inform. 53, 405414 (2015).
Article PubMed Google Scholar
Srivastava, S., Chatziefthymiou, S. D. & Kolbe, M. Vaccines Targeting Numerous Coronavirus Antigens, Ensuring Broader Global Population Coverage: Multi-epitope and Multi-patch Vaccines. In Vaccine Design: Methods and Protocols, Volume 1. Vaccines for Human Diseases. Methods in Molecular Biology. (ed. Thomas, S.) 149175 (Springer US, 2022).
Vita, R. et al. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 47, D339D343 (2019).
Article CAS PubMed Google Scholar
Dimitrov, I., Zaharieva, N. & Doytchinova, I. Bacterial immunogenicity prediction by machine learning methods. Vaccines 8, 709 (2020).
Article PubMed PubMed Central Google Scholar
Ong, E. et al. Vaxign2: the second generation of the first web-based vaccine design program using reverse vaccinology and machine learning. Nucleic Acids Res. 49, W671W678 (2021).
Article CAS PubMed PubMed Central Google Scholar
Herrera-Bravo, J. et al. VirVACPRED: a web server for prediction of protective viral antigens. Int. J. Pept. Res. Ther. 28, 35 (2021).
Article PubMed PubMed Central Google Scholar
Bowman, B. N. et al. Improving reverse vaccinology with a machine learning approach. Vaccine 29, 81568164 (2011).
Article PubMed Google Scholar
Heinson, A. I. et al. Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. Int. J. Mol. Sci. 18, 312 (2017).
Article PubMed PubMed Central Google Scholar
Ong, E. et al. Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics 36, 31853191 (2020).
Article CAS PubMed PubMed Central Google Scholar
Ong, E., Wong, MU., Huffman, A. & He, Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front. Immunol. 11, 1581 (2020).
Yarmarkovich, M., Warrington, J. M., Farrel, A. & Maris, J. M. Identification of SARS-CoV-2 vaccine epitopes predicted to induce long-term population-scale immunity. Cell Rep. Med. 1, 100036 (2020).
Article CAS PubMed PubMed Central Google Scholar
Yang, Z., Bogdan, P. & Nazarian, S. An in silico deep learning approach to multi-epitope vaccine design: A SARS-CoV-2 case study. Sci. Rep. 11, 3238 (2021).
Article CAS PubMed PubMed Central Google Scholar
Mohanty, E. & Mohanty, A. Role of artificial intelligence in peptide vaccine design against RNA Viruses. Inf. Med. Unlocked 26, 100768 (2021).
Article Google Scholar
Swadling, L. et al. Pre-existing polymerase-specific T cells expand in abortive seronegative SARS-CoV-2. Nature 601, 110117 (2022).
Article CAS PubMed Google Scholar
Mei, S. et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 21, 11191135 (2019).
Article Google Scholar
Nielsen, M., Andreatta, M., Peters, B. & Buus, S. Immunoinformatics: predicting peptideMHC binding. Annu. Rev. Biomed. Data Sci. 3, 191215 (2020).
Article PubMed PubMed Central Google Scholar
Kar, P., Ruiz-Perez, L., Arooj, M. & Mancera, R. L. Current methods for the prediction of T-cell epitopes. Pept. Sci. 110, e24046 (2018).
Buckley, P. R. et al. Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens. Brief. Bioinform. 23, bbac141 (2022).
Article PubMed PubMed Central Google Scholar
Lee, C. H. et al. Predicting cross-reactivity and antigen specificity of T cell receptors. Front. Immunol. 11, 565096 (2020).
Article CAS PubMed PubMed Central Google Scholar
Norman, R. A. et al. Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief. Bioinform. 21, 15491567 (2020).
Article PubMed Google Scholar
Kim, J., McFee, M., Fang, Q., Abdin, O. & Kim, P. M. Computational and artificial intelligence-based methods for antibody development. Trends Pharmacol. Sci. 44, 175189 (2023).
Article CAS PubMed Google Scholar
Shugay, M. et al. VDJdb: a curated database of t-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 46, D419D427 (2018).
Article CAS PubMed Google Scholar
Dunbar, J. et al. SAbDab: the structural antibody database. Nucleic Acids Res. 42, D11401146 (2014).
Article CAS PubMed Google Scholar
Saha, S. & Raghava, G. P. S. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65, 4048 (2006).
Article CAS PubMed Google Scholar
Rubinstein, N. D., Mayrose, I. & Pupko, T. A machine-learning approach for predicting B-cell epitopes. Mol. Immunol. 46, 840847 (2009).
Article CAS PubMed Google Scholar
Zhao, L., Wong, L., Lu, L., Hoi, S. C. & Li, J. B-cell epitope prediction through a graph model. BMC Bioinform. 13, S20 (2012).
Article Google Scholar
Jespersen, M. C., Peters, B., Nielsen, M. & Marcatili, P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 45, W24W29 (2017).
Article CAS PubMed PubMed Central Google Scholar
Clifford, J. N. et al. BepiPred-3.0: improved B-cell epitope prediction using protein language models. Protein Sci.: Publ. Protein Soc. 31, e4497 (2022).
Article Google Scholar
Liu, T., Shi, K. & Li, W. Deep learning methods improve linear B-cell epitope prediction. BioData Mining 13, 1 (2020).
Article PubMed PubMed Central Google Scholar
da Silva, B. M., Myung, Y., Ascher, D. B. & Pires, D. E. V. epitope3D: a machine learning method for conformational B-cell epitope prediction. Brief. Bioinform. 23, bbab423 (2022).
Article PubMed Google Scholar
Shashkova, T. I. et al. SEMA: antigen B-cell conformational epitope prediction using deep transfer learning. Front. Immunol. 13, 960985 (2022).
Tubiana, J., Schneidman-Duhovny, D. & Wolfson, H. J. ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction. Nat. Methods 19, 730739 (2022).
Hie, M. H. et al. DiscoTope-3.0 - improved B-celL epitope prediction using AlphaFold2 modeling and inverse folding latent representations. bioRxiv https://doi.org/10.1101/2023.02.05.527174 (2023).
Parker, J. M., Guo, D. & Hodges, R. S. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25, 54255432 (1986).
Article CAS PubMed Google Scholar
Kolaskar, A. S. & Tongaonkar, P. C. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 276, 172174 (1990).
Article CAS PubMed Google Scholar
Karplus, P. A. & Schulz, G. E. Prediction of chain flexibility in proteins. Naturwissenschaften 72, 212213 (1985).
Article CAS Google Scholar
Thornton, J. M., Edwards, M. S., Taylor, W. R. & Barlow, D. J. Location of continuous antigenic determinants in the protruding regions of proteins. EMBO J. 5, 409413 (1986).
Article CAS PubMed PubMed Central Google Scholar
Ponomarenko, J. et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 9, 514 (2008).
Article Google Scholar
Emini, E. A., Hughes, J. V., Perlow, D. S. & Boger, J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J. Virol. 55, 836839 (1985).
Article CAS PubMed PubMed Central Google Scholar
Ingraham, J., Garg, V. K., Barzilay, R. & Jaakkola, T. Generative Models for Graph-Based Protein Design. NIPS 2019 (2019).
Strokach, A., Becerra, D., Corbi-Verge, C. & Kim, P. M. Fast and flexible protein design using deep graph neural networks. Cell Syst. 11, 402411.e4 (2020).
Fout, A., Byrd, J., Shariat, B. & Ben-Hur A. Protein interface prediction using graph convolutional networks. In: Advances in Neural Information Processing Systems. vol. 30 (Curran Associates, Inc., 2017).
Yuan, Q., Chen, J., Zhao, H., Zhou, Y. & Yang, Y. Structure-aware proteinprotein interaction site prediction using deep graph convolutional network. Bioinformatics 38, 125132 (2021).
Article PubMed Google Scholar
Abdollahi, N., Tonekaboni, S. A. M., Huang, J., Wang, B. & MacKinnon, S. NodeCoder: a graph-based machine learning platform to predict active sites of modeled protein structures. arXiv https://doi.org/10.48550/arXiv.2302.03590 (2023).
Cha, M. et al. Unifying structural descriptors for biological and bioinspired nanoscale complexes. Nat. Comput. Sci. 2, 243252 (2022).
Article PubMed Google Scholar
Roche, R., Moussad, B., Shuvo, M. H. & Bhattacharya, D. E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction. PLoS Comput. Biol. 19, e1011435 (2023).
Article CAS PubMed PubMed Central Google Scholar
Ferreira, M. V., Nogueira, T., Rios, R. A., Lopes, T. J. S. A graph-based machine learning framework identifies critical properties of FVIII that lead to Hemophilia A. Front. Bioinform. 3, 1152039 (2023).
Zhou, J. et al. Graph neural networks: a review of methods and applications. AI Open 1, 5781 (2020).
Article Google Scholar
Hsu, C. et al. Learning inverse folding from millions of predicted structures. In: Proceedings of the 39th International Conference on Machine Learning. p. 89468970 (PMLR, 2022).
Muhammed, M. T. & Aki-Yalcin, E. Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem. Biol. Drug Des. 93, 1220 (2019).
Article CAS PubMed Google Scholar
Ambrosetti, F., Jimnez-Garca, B., Roel-Touris, J. & Bonvin, A. M. J. J. Modeling antibody-antigen complexes by information-driven docking. Structure 28, 119129.e2 (2020).
Article PubMed Google Scholar
Schoeder, C. T. et al. Modeling immunity with rosetta: methods for antibody and antigen design. Biochemistry 60, 825846 (2021).
Peacock, T. & Chain, B. Information-driven docking for TCR-pMHC complex prediction. Front. Immunol. 12, 686127 (2021).
Atanasova, M. & Doytchinova, I. Docking-based prediction of peptide binding to MHC proteins. Methods Mol. Biol. 2673, 237249 (2023).
Article CAS PubMed Google Scholar
Dormitzer, P. R., Ulmer, J. B. & Rappuoli, R. Structure-based antigen design: a strategy for next generation vaccines. Trends Biotechnol. 26, 659667 (2008).
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Development and use of machine learning algorithms in vaccine target selection | npj Vaccines - Nature.com