[1] Murphy, K., Weaver, C.: Janeway's immunobiology. Garland science (2016)

[2] Cooper, M.D.: The early history of B cells. Nat. Rev. Immunol. 15(3), 191–197 (2015)

[3] Ratajczak, W., et al.: Immunological memory cells. Central Eur. J. Immunol. 43(2), 194–203 (2018)

[4] Zhang, J., et al.: Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering. Biomed. Res. Int. 17(2014), 689219 (2014)

[5] Janeway, C., et al.: Immunobiology: the immune system in health and disease, vol 2. Garland Pub. New York (2001)

[6] Al Qaraghuli, M.M., et al.: Antibody-protein binding and conformational changes: identifying allosteric signalling pathways to engineer a better effector response. Sci. Rep. 10(1), 13696 (2020)

[7] Kulkarni-Kale, U., Bhosle, S., Kolaskar, A.S.: CEP: a conformational epitope prediction server. Nucleic Acids Res. 33(Web Server issue), W168–71 (2005)

[8] Strokach, A., et al.: Fast and flexible protein design using deep graph neural networks. Cell Syst. 11(4), 402–411 e4 (2020)

[9] Jespersen, M.C., et al.: BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 45(W1), W24–W29 (2017)

[10] Klausen, M.S., et al.: LYRA, a webserver for lymphocyte receptor structural modeling 43(W1), W349–W355 (2015)

[11] Evans, R., et al.: Protein complex prediction with AlphaFold-Multimer. biorxiv 463034 (2021)

[12] Jumper, J., et al.: Highly accurate protein structure prediction with AlphaFold. Nature 596(7873), 583–589 (2021)

[13] Collatz, M., et al.: EpiDope: a deep neural network for linear B-cell epitope prediction. Bioinformatics 37(4), 448–455 (2021)

[14] Clifford, J.N., et al.: BepiPred-3.0: improved B-cell epitope prediction using protein language models. Protein Sci. 31(12), e4497 (2022)

[15] Israeli, S., Louzoun, Y.: Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections. Brief Bioinform. 25(2), bbae084 (2024)

[16] Vita, R., et al.: The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 43(Database issue), D405–12 (2015)

[17] Vita, R., et al.: The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 47(D1), D339–D343 (2019)

[18] Fu, L., et al.: CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23), 3150–3152 (2012)

[19] Di Gennaro, G., Buonanno, A., Palmieri, F.A.N.: Considerations about learning Word2Vec. J. Supercomput. 77(11), 12320–12335 (2021). https://doi.org/10.1007/s11227-021-03743-2

[20] Collatz, M., et al.: EpiDope: a deep neural network for linear B-cell epitope prediction 37(4), 448–455 (2021)

[21] Hasan, M.M., et al.: ILBE for computational identification of linear B-cell epitopes by integrating sequence and evolutionary features. Genom. Proteom. Bioinform. 18(5), 593–600 (2020)

[22] Galanis, K.A., et al.: Linear B-cell epitope prediction for in silico vaccine design: a performance review of methods available via command-line interface. Int. J. Mol. Sci. 22(6), 3210 (2021)

[23] Hu, X., et al.: Deep learning frameworks for protein–protein interaction prediction. Comput. Struct. Biotechnol. J. 20, 3223–3233 (2022)

[24] Liu, T., Shi, K., Li, W.: Deep learning methods improve linear B-cell epitope prediction. BioData Min 13, 1 (2020)

[25] Zeng, X., et al.: Recent progress in antibody epitope prediction. Antibodies (Basel) 12(3), 52 (2023)