Chen, Y. et al. Human infections with the emerging avian influenza A H7N9 virus from wet market poultry: Clinical analysis and characterisation of viral genome. Lancet (London, England) 381, 1916–1925. https://doi.org/10.1016/s0140-6736(13)60903-4 (2013).
Google Scholar
Dortmans, J. C. et al. Adaptation of novel H7N9 influenza A virus to human receptors. Sci. Rep. 3, 3058. https://doi.org/10.1038/srep03058 (2013).
Google Scholar
Su, S. et al. Epidemiology, evolution, and pathogenesis of H7N9 influenza viruses in five epidemic waves since 2013 in China. Trends Microbiol. 25, 713–728. https://doi.org/10.1016/j.tim.2017.06.008 (2017).
Google Scholar
Vasin, A. V. et al. Molecular mechanisms enhancing the proteome of influenza A viruses: An overview of recently discovered proteins. Virus Res. 185, 53–63. https://doi.org/10.1016/j.virusres.2014.03.015 (2014).
Google Scholar
Petrova, V. N. & Russell, C. A. The evolution of seasonal influenza viruses. Nat. Rev. Microbiol. 16, 47–60. https://doi.org/10.1038/nrmicro.2017.118 (2018).
Google Scholar
Liu, D. et al. Origin and diversity of novel avian influenza A H7N9 viruses causing human infection: Phylogenetic, structural, and coalescent analyses. The Lancet 381, 1926–1932. https://doi.org/10.1016/S0140-6736(13)60938-1 (2013).
Google Scholar
Su, S. et al. Epidemiology, evolution, and recent outbreaks of avian influenza virus in China. J. Virol. 89, 8671–8676. https://doi.org/10.1128/jvi.01034-15 (2015).
Google Scholar
Zhang, J. et al. Evolution and antigenic drift of influenza A (H7N9) viruses, China, 2017–2019. Emerg. Infect. Dis. 26, 1906–1911. https://doi.org/10.3201/eid2608.200244 (2020).
Google Scholar
Su, S. et al. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol. 24, 490–502. https://doi.org/10.1016/j.tim.2016.03.003 (2016).
Google Scholar
Nachbagauer, R. et al. A chimeric hemagglutinin-based universal influenza virus vaccine approach induces broad and long-lasting immunity in a randomized, placebo-controlled phase I trial. Nat. Med. 27, 106–114. https://doi.org/10.1038/s41591-020-1118-7 (2021).
Google Scholar
Du, L. et al. A critical HA1 neutralizing domain of H5N1 influenza in an optimal conformation induces strong cross-protection. PLoS ONE 8, e53568. https://doi.org/10.1371/journal.pone.0053568 (2013).
Google Scholar
Aguilar-Yáñez, J. M. et al. An influenza A/H1N1/2009 hemagglutinin vaccine produced in Escherichia coli. PLoS ONE 5, e11694. https://doi.org/10.1371/journal.pone.0011694 (2010).
Google Scholar
To, K. K. et al. Recombinant influenza A virus hemagglutinin HA2 subunit protects mice against influenza A(H7N9) virus infection. Arch. Virol. 160, 777–786. https://doi.org/10.1007/s00705-014-2314-x (2015).
Google Scholar
Fan, X. et al. Targeting the HA2 subunit of influenza A virus hemagglutinin via CD40L provides universal protection against diverse subtypes. Mucosal Immunol. 8, 211–220. https://doi.org/10.1038/mi.2014.59 (2015).
Google Scholar
Yang, H., Carney, P. J., Chang, J. C., Villanueva, J. M. & Stevens, J. Structural analysis of the hemagglutinin from the recent 2013 H7N9 influenza virus. J. Virol. 87, 12433. https://doi.org/10.1128/JVI.01854-13 (2013).
Google Scholar
Wagner, R., Matrosovich, M. & Klenk, H.-D. Functional balance between haemagglutinin and neuraminidase in influenza virus infections. Rev. Med. Virol. 12, 159–166. https://doi.org/10.1002/rmv.352 (2002).
Google Scholar
Bangaru, S. et al. A site of vulnerability on the influenza virus hemagglutinin head domain trimer interface. Cell 177, 1136-1152.e1118. https://doi.org/10.1016/j.cell.2019.04.011 (2019).
Google Scholar
Xu, Y. et al. Avian-to-human receptor-binding adaptation of avian H7N9 influenza virus hemagglutinin. Cell Rep. 29, 2217-2228.e2215. https://doi.org/10.1016/j.celrep.2019.10.047 (2019).
Google Scholar
de Vries, R. P. et al. Three mutations switch H7N9 influenza to human-type receptor specificity. PLoS Pathog. 13, e1006390. https://doi.org/10.1371/journal.ppat.1006390 (2017).
Google Scholar
Xu, Q., Chen, Z., Cheng, X., Xu, L. & Jin, H. Evaluation of live attenuated H7N3 and H7N7 vaccine viruses for their receptor binding preferences, immunogenicity in ferrets and cross reactivity to the novel H7N9 virus. PLoS ONE 8, e76884. https://doi.org/10.1371/journal.pone.0076884 (2013).
Google Scholar
Gao, R. et al. Human infection with a novel avian-origin influenza A (H7N9) virus. N. Engl. J. Med. 368, 1888–1897. https://doi.org/10.1056/NEJMoa1304459 (2013).
Google Scholar
Rudenko, L. et al. H7N9 live attenuated influenza vaccine in healthy adults: A randomised, double-blind, placebo-controlled, phase 1 trial. Lancet Infect Dis. 16, 303–310. https://doi.org/10.1016/S1473-3099(15)00378-3 (2016).
Google Scholar
Stadlbauer, D. et al. AS03-adjuvanted H7N9 inactivated split virion vaccines induce cross-reactive and protective responses in ferrets. npj Vaccines 6, 40. https://doi.org/10.1038/s41541-021-00299-3 (2021).
Google Scholar
Oshansky, C. M. et al. Adjuvanted recombinant hemagglutinin H7 vaccine to highly pathogenic influenza A(H7N9) elicits high and sustained antibody responses in healthy adults. npj Vaccines 6, 41. https://doi.org/10.1038/s41541-021-00287-7 (2021).
Google Scholar
Gilchuk, I. M. et al. Influenza H7N9 virus neuraminidase-specific human monoclonal antibodies inhibit viral egress and protect from lethal influenza infection in mice. Cell Host Microbe 26, 715-728.e718. https://doi.org/10.1016/j.chom.2019.10.003 (2019).
Google Scholar
Solanki, V., Tiwari, M. & Tiwari, V. Prioritization of potential vaccine targets using comparative proteomics and designing of the chimeric multi-epitope vaccine against Pseudomonas aeruginosa. Sci. Rep. 9, 5240. https://doi.org/10.1038/s41598-019-41496-4 (2019).
Google Scholar
Shey, R. A. et al. In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases. Sci. Rep. 9, 4409. https://doi.org/10.1038/s41598-019-40833-x (2019).
Google Scholar
Corder, B. N., Bullard, B. L., Poland, G. A. & Weaver, E. A. A decade in review: A systematic review of universal influenza vaccines in clinical trials during the 2010 decade. Viruses https://doi.org/10.3390/v12101186 (2020).
Google Scholar
Nelson, M. I. & Vincent, A. L. Reverse zoonosis of influenza to swine: New perspectives on the human–animal interface. Trends Microbiol. 23, 142–153. https://doi.org/10.1016/j.tim.2014.12.002 (2015).
Google Scholar
Watanabe, T., Watanabe, S., Maher, E. A., Neumann, G. & Kawaoka, Y. Pandemic potential of avian influenza A (H7N9) viruses. Trends Microbiol. 22, 623–631. https://doi.org/10.1016/j.tim.2014.08.008 (2014).
Google Scholar
Xu, J., Lu, S., Wang, H. & Chen, C. Reducing exposure to avian influenza H7N9. The Lancet 381, 1815–1816. https://doi.org/10.1016/S0140-6736(13)60950-2 (2013).
Google Scholar
Wu, A. et al. Sequential reassortments underlie diverse influenza H7N9 genotypes in China. Cell Host Microbe 14, 446–452. https://doi.org/10.1016/j.chom.2013.09.001 (2013).
Google Scholar
Lu, S. et al. Prognosis of 18 H7N9 avian influenza patients in Shanghai. PLoS ONE 9, e88728. https://doi.org/10.1371/journal.pone.0088728 (2014).
Google Scholar
Tong, S. et al. New world bats harbor diverse influenza A viruses. PLoS Pathog. 9, e1003657. https://doi.org/10.1371/journal.ppat.1003657 (2013).
Google Scholar
Murphy, B. R. et al. Avian-human reassortant influenza A viruses derived by mating avian and human influenza A viruses. J. Infect. Dis. 150, 841–850. https://doi.org/10.1093/infdis/150.6.841 (1984).
Google Scholar
Zhao, R. et al. Identification of a highly conserved H1 subtype-specific epitope with diagnostic potential in the hemagglutinin protein of influenza A virus. PLoS ONE 6, e23374–e23374. https://doi.org/10.1371/journal.pone.0023374 (2011).
Google Scholar
Bui, C. M., Gardner, L., MacIntyre, R. & Sarkar, S. Influenza A H5N1 and H7N9 in China: A spatial risk analysis. PLoS ONE 12, e0174980. https://doi.org/10.1371/journal.pone.0174980 (2017).
Google Scholar
Sette, A. & Fikes, J. Epitope-based vaccines: An update on epitope identification, vaccine design and delivery. Curr. Opin. Immunol. 15, 461–470. https://doi.org/10.1016/s0952-7915(03)00083-9 (2003).
Google Scholar
Usman Mirza, M. et al. Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins. Sci. Rep. 6, 37313. https://doi.org/10.1038/srep37313 (2016).
Google Scholar
Moise, L. et al. T cell epitope engineering: An avian H7N9 influenza vaccine strategy for pandemic preparedness and response. Hum. Vac. Immunother. 14, 2203–2207. https://doi.org/10.1080/21645515.2018.1495303 (2018).
Google Scholar
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. https://doi.org/10.3390/ijms22063210 (2021).
Google Scholar
Wang, X. et al. Evaluation and comparison of newly built linear B-Cell epitope prediction software from a users’ perspective. Curr. Bioinform. 13, 149–156. https://doi.org/10.2174/1574893612666170711154318 (2018).
Google Scholar
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M. & Marcatili, P. Antibody specific B-cell epitope predictions: leveraging information from antibody-antigen protein complexes. Front. Immunol. https://doi.org/10.3389/fimmu.2019.00298 (2019).
Google Scholar
Russi, R. C., Bourdin, E., García, M. I. & Veaute, C. M. I. In silico prediction of T- and B-cell epitopes in PmpD: First step towards to the design of a Chlamydia trachomatis vaccine. Biomed. J. 41, 109–117. https://doi.org/10.1016/j.bj.2018.04.007 (2018).
Google Scholar
Kringelum, J. V., Nielsen, M., Padkjær, S. B. & Lund, O. Structural analysis of B-cell epitopes in antibody:protein complexes. Mol. Immunol. 53, 24–34. https://doi.org/10.1016/j.molimm.2012.06.001 (2013).
Google Scholar
Yu, F. et al. A Potent Germline-like human monoclonal antibody targets a pH-sensitive epitope on H7N9 influenza hemagglutinin. Cell Host Microbe 22, 471-483.e475. https://doi.org/10.1016/j.chom.2017.08.011 (2017).
Google Scholar
Huang, K.-Y.A. et al. Structure–function analysis of neutralizing antibodies to H7N9 influenza from naturally infected humans. Nat. Microbiol. 4, 306–315. https://doi.org/10.1038/s41564-018-0303-7 (2019).
Google Scholar
Gerhard, W., Yewdell, J., Frankel, M. E. & Webster, R. Antigenic structure of influenza virus haemagglutinin defined by hybridoma antibodies. Nature 290, 713–717. https://doi.org/10.1038/290713a0 (1981).
Google Scholar
Caton, A. J., Brownlee, G. G., Yewdell, J. W. & Gerhard, W. The antigenic structure of the influenza virus A/PR/8/34 hemagglutinin (H1 subtype). Cell 31, 417–427. https://doi.org/10.1016/0092-8674(82)90135-0 (1982).
Google Scholar
Zuo, T. et al. Comprehensive analysis of antibody recognition in convalescent humans from highly pathogenic avian influenza H5N1 infection. Nat. Commun. 6, 8855. https://doi.org/10.1038/ncomms9855 (2015).
Google Scholar
Kallewaard, N. L. et al. Structure and function analysis of an antibody recognizing all influenza A subtypes. Cell 166, 596–608. https://doi.org/10.1016/j.cell.2016.05.073 (2016).
Google Scholar
DiLillo, D. J., Tan, G. S., Palese, P. & Ravetch, J. V. Broadly neutralizing hemagglutinin stalk-specific antibodies require FcγR interactions for protection against influenza virus in vivo. Nat. Med. 20, 143–151. https://doi.org/10.1038/nm.3443 (2014).
Google Scholar
Henry Dunand, C. J. et al. Both neutralizing and non-neutralizing human H7N9 influenza vaccine-induced monoclonal antibodies confer protection. Cell Host Microbe 19, 800–813. https://doi.org/10.1016/j.chom.2016.05.014 (2016).
Google Scholar
Tan, G. S. et al. Broadly-reactive neutralizing and non-neutralizing antibodies directed against the H7 influenza virus hemagglutinin reveal divergent mechanisms of protection. PLoS Pathog. 12, e1005578. https://doi.org/10.1371/journal.ppat.1005578 (2016).
Google Scholar
Zheng, D. et al. Influenza H7N9 LAH-HBc virus-like particle vaccine with adjuvant protects mice against homologous and heterologous influenza viruses. Vaccine 34, 6464–6471. https://doi.org/10.1016/j.vaccine.2016.11.026 (2016).
Google Scholar
De Groot, A. S., Moise, L., McMurry, J. A. & Martin, W. Epitope-based immunome-derived vaccines: A strategy for improved design and safety. Clin. Appl. Immunomics 2, 39–69. https://doi.org/10.1007/978-0-387-79208-8_3 (2008).
Google Scholar
Davies, M. N. & Flower, D. R. Harnessing bioinformatics to discover new vaccines. Drug Discovery Today 12, 389–395. https://doi.org/10.1016/j.drudis.2007.03.010 (2007).
Google Scholar
Toledo, H. et al. A phase I clinical trial of a multi-epitope polypeptide TAB9 combined with montanide ISA 720 adjuvant in non-HIV-1 infected human volunteers. Vaccine 19, 4328–4336. https://doi.org/10.1016/s0264-410x(01)00111-6 (2001).
Google Scholar
Zhou, W. Y. et al. Therapeutic efficacy of a multi-epitope vaccine against Helicobacter pylori infection in BALB/c mice model. Vaccine 27, 5013–5019. https://doi.org/10.1016/j.vaccine.2009.05.009 (2009).
Google Scholar
Chen, X., Zaro, J. L. & Shen, W.-C. Fusion protein linkers: Property, design and functionality. Adv. Drug Deliv. Rev. 65, 1357–1369. https://doi.org/10.1016/j.addr.2012.09.039 (2013).
Google Scholar
Yang, Y. et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum. Vac. Immunother. 11, 795–805. https://doi.org/10.1080/21645515.2015.1012017 (2015).
Google Scholar
Reddy Chichili, V. P., Kumar, V. & Sivaraman, J. Linkers in the structural biology of protein–protein interactions. Protein Sci. 22, 153–167. https://doi.org/10.1002/pro.2206 (2013).
Google Scholar
Athanasiou, E. et al. A poly(Lactic-co-Glycolic) acid nanovaccine based on chimeric peptides from different leishmania infantum proteins induces dendritic cells maturation and promotes peptide-specific IFNγ-producing CD8+ t cells essential for the protection against experiment. Front. Immunol. https://doi.org/10.3389/fimmu.2017.00684 (2017).
Google Scholar
Nezafat, N., Ghasemi, Y., Javadi, G., Khoshnoud, M. J. & Omidinia, E. A novel multi-epitope peptide vaccine against cancer: An in silico approach. J. Theor. Biol. 349, 121–134. https://doi.org/10.1016/j.jtbi.2014.01.018 (2014).
Google Scholar
Dong, R., Chu, Z., Yu, F. & Zha, Y. Contriving multi-epitope subunit of vaccine for COVID-19: Immunoinformatics approaches. Front. Immunol. https://doi.org/10.3389/fimmu.2020.01784 (2020).
Google Scholar
Lennon-Duménil, A. M., Bakker, A. H., Wolf-Bryant, P., Ploegh, H. L. & Lagaudrière-Gesbert, C. A closer look at proteolysis and MHC-class-II-restricted antigen presentation. Curr. Opin. Immunol. 14, 15–21. https://doi.org/10.1016/s0952-7915(01)00293-x (2002).
Google Scholar
Yano, A. et al. An ingenious design for peptide vaccines. Vaccine 23, 2322–2326. https://doi.org/10.1016/j.vaccine.2005.01.031 (2005).
Google Scholar
Safavi, A., Kefayat, A., Mahdevar, E., Abiri, A. & Ghahremani, F. Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches. Vaccine 38, 7612–7628. https://doi.org/10.1016/j.vaccine.2020.10.016 (2020).
Google Scholar
Vogel, F. R. Improving vaccine performance with adjuvants. Clin. Infect. Dis. 30, S266–S270. https://doi.org/10.1086/313883 (2000).
Google Scholar
Pulendran, B. & Maddur, M. S. in Influenza Pathogenesis and Control – Volume II (eds Michael B. A. Oldstone & Richard W. Compans) 23–71 (Springer, 2015).
Jeisy-Scott, V. et al. TLR7 recognition is dispensable for influenza virus A infection but important for the induction of hemagglutinin-specific antibodies in response to the 2009 pandemic split vaccine in mice. J. Virol. 86, 10988–10998. https://doi.org/10.1128/JVI.01064-12 (2012).
Google Scholar
Wille-Reece, U., Wu, C. Y., Flynn, B. J., Kedl, R. M. & Seder, R. A. Immunization with HIV-1 Gag protein conjugated to a TLR7/8 agonist results in the generation of HIV-1 Gag-specific Th1 and CD8+ T cell responses. J. Immunol. (Baltimore, Md.: 1950) 174, 7676–7683. https://doi.org/10.4049/jimmunol.174.12.7676 (2005).
Google Scholar
Miller, S. M. et al. Investigation of novel TLR7/8 ligands in combination with TLR4 ligands as adjuvants to drive cell mediated anti-influenza immunity. J. Immunol. 200, 125.116 (2018).
Georg, P. & Sander, L. E. Innate sensors that regulate vaccine responses. Curr. Opin. Immunol. 59, 31–41. https://doi.org/10.1016/j.coi.2019.02.006 (2019).
Google Scholar
Zhang, Z. et al. Structural analyses of toll-like receptor 7 reveal detailed RNA sequence specificity and recognition mechanism of agonistic ligands. Cell Rep. 25, 3371-3381.e3375. https://doi.org/10.1016/j.celrep.2018.11.081 (2018).
Google Scholar
Tanji, H. et al. Toll-like receptor 8 senses degradation products of single-stranded RNA. Nat. Struct. Mol. Biol. 22, 109–115. https://doi.org/10.1038/nsmb.2943 (2015).
Google Scholar
Heil, F. et al. Species-specific recognition of single-stranded RNA via toll-like receptor 7 and 8. Science (New York, N.Y.) 303, 1526–1529. https://doi.org/10.1126/science.1093620 (2004).
Google Scholar
Ugolini, M. et al. Recognition of microbial viability via TLR8 drives TFH cell differentiation and vaccine responses. Nat. Immunol. 19, 386–396. https://doi.org/10.1038/s41590-018-0068-4 (2018).
Google Scholar
de Marcken, M. & Dhaliwal, K. TLR7 and TLR8 activate distinct pathways in monocytes during RNA virus infection. Sci. Signal. https://doi.org/10.1126/scisignal.aaw1347 (2019).
Google Scholar
Harder, J., Bartels, J., Christophers, E. & Schröder, J.-M. Isolation and characterization of human µ-Defensin-3, a novel human inducible peptide antibiotic. J. Biol. Chem. 276, 5707–5713. https://doi.org/10.1074/jbc.M008557200 (2001).
Google Scholar
Funderburg, N. et al. Human β-defensin-3 activates professional antigen-presenting cells via Toll-like receptors 1 and 2. Proc. Natl. Acad. Sci. 104, 18631–18635. https://doi.org/10.1073/pnas.0702130104 (2007).
Google Scholar
Judge, C. J. et al. HBD-3 induces NK cell activation, IFN-γ secretion and mDC dependent cytolytic function. Cell. Immunol. 297, 61–68. https://doi.org/10.1016/j.cellimm.2015.06.004 (2015).
Google Scholar
Netea, M. G., Van der Meer, J. W. M., Sutmuller, R. P., Adema, G. J. & Kullberg, B.-J. From the Th1/Th2 paradigm towards a toll-like receptor/T-helper bias. Antimicrobial Agents Chemother. 49, 3991. https://doi.org/10.1128/AAC.49.10.3991-3996.2005 (2005).
Google Scholar
Xu, D., Tsai, C. J. & Nussinov, R. Hydrogen bonds and salt bridges across protein-protein interfaces. Protein Eng. 10, 999–1012. https://doi.org/10.1093/protein/10.9.999 (1997).
Google Scholar
Xie, N.-Z., Du, Q.-S., Li, J.-X. & Huang, R.-B. Exploring strong interactions in proteins with quantum chemistry and examples of their applications in drug design. PLoS ONE 10, e0137113. https://doi.org/10.1371/journal.pone.0137113 (2015).
Google Scholar
Chen, W. H. et al. Antibody and Th1-type cell-mediated immune responses in elderly and young adults immunized with the standard or a high dose influenza vaccine. Vaccine 29, 2865–2873. https://doi.org/10.1016/j.vaccine.2011.02.017 (2011).
Google Scholar
Palladino, G., Scherle, P. A. & Gerhard, W. Activity of CD4+ T-cell clones of type 1 and type 2 in generation of influenza virus-specific cytotoxic responses in vitro. J. Virol. 65, 6071–6076. https://doi.org/10.1128/jvi.65.11.6071-6076.1991 (1991).
Google Scholar
Wang, Z. et al. Recovery from severe H7N9 disease is associated with diverse response mechanisms dominated by CD8+ T cells. Nat. Commun. 6, 6833–6833. https://doi.org/10.1038/ncomms7833 (2015).
Google Scholar
Wang, Z. et al. Early hypercytokinemia is associated with interferon-induced transmembrane protein-3 dysfunction and predictive of fatal H7N9 infection. Proc. Natl. Acad. Sci. USA 111, 769–774. https://doi.org/10.1073/pnas.1321748111 (2014).
Google Scholar
To, K. K. et al. Human H7N9 virus induces a more pronounced pro-inflammatory cytokine but an attenuated interferon response in human bronchial epithelial cells when compared with an epidemiologically-linked chicken H7N9 virus. Virol. J. 13, 42. https://doi.org/10.1186/s12985-016-0498-2 (2016).
Google Scholar
Liu, R. et al. H7N9 T-cell epitopes that mimic human sequences are less immunogenic and may induce Treg-mediated tolerance. Hum. Vac. Immunother. 11, 2241–2252. https://doi.org/10.1080/21645515.2015.1052197 (2015).
Google Scholar
Arilahti, V., Mäkelä, S. M., Tynell, J., Julkunen, I. & Österlund, P. Novel avian influenza A (H7N9) virus induces impaired interferon responses in human dendritic cells. PLoS ONE 9, e96350. https://doi.org/10.1371/journal.pone.0096350 (2014).
Google Scholar
Zhu, H. et al. Infectivity, transmission, and pathology of human-isolated H7N9 influenza virus in ferrets and pigs. Science (New York, N.Y.) 341, 183–186. https://doi.org/10.1126/science.1239844 (2013).
Google Scholar
Jespersen, M. C., Peters, B., Nielsen, M. & Marcatili, P. BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucl. Acids Res. 45, W24–W29. https://doi.org/10.1093/nar/gkx346 (2017).
Google Scholar
Manavalan, B., Govindaraj, R. G., Shin, T. H., Kim, M. O. & Lee, G. iBCE-EL: A new ensemble learning framework for improved linear B-cell epitope prediction. Front. Immunol. https://doi.org/10.3389/fimmu.2018.01695 (2018).
Google Scholar
Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 4. https://doi.org/10.1186/1471-2105-8-4 (2007).
Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science (New York, N.Y.) 373, 871–876. https://doi.org/10.1126/science.abj8754 (2021).
Google Scholar

