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).      
Read the original here:
Development and use of machine learning algorithms in vaccine target selection | npj Vaccines - Nature.com