Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Bioinformatics design and experimental validation of influenza A virus multi-epitopes that induce neutralizing antibodies


Pandemics caused by influenza A virus (IAV) are responsible for the deaths of millions of humans around the world. One of these pandemics occurred in Mexico in 2009. Despite the impact of IAV on human health, there is no effective vaccine. Gene mutations and translocation of genome segments of different IAV subtypes infecting a single host cell make the development of a universal vaccine difficult. The design of immunogenic peptides using bioinformatics tools could be an interesting strategy to increase the success of vaccines. In this work, we used the predicted amino acid sequences of the neuraminidase (NA) and hemagglutinin (HA) proteins of different IAV subtypes to perform multiple alignments, epitope predictions, molecular dynamics simulations, and experimental validation. Peptide selection was based on the following criteria: promiscuity, protein surface exposure, and the degree of conservation among different medically relevant IAV strains. These peptides were tested using immunological assays to test their ability to induce production of antibodies against IAV. We immunized rabbits and mice and measured the levels of IgG and IgA antibodies in serum samples and nasal washes. Rabbit antibodies against the peptides P11 and P14 (both of which are hybrids of NA and HA) recognized HA from both group 1 (H1, H2, and H5) and group 2 (H3 and H7) IAV and also recognized the purified NA protein from the viral stock (influenza A Puerto Rico/916/34). IgG antibodies from rabbits immunized with P11 and P14 were capable of recognizing viral particles and inhibited virus hemagglutination. Additionally, intranasal immunization of mice with P11 and P14 induced specific IgG and IgA antibodies in serum and nasal mucosa, respectively. Interestingly, the IgG antibodies were found to have neutralizing capability. In conclusion, the peptides designed through in silico studies were validated in experimental assays.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2: A
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    Szewczyk B, Bienkowska-Szewczyk K, Krol E (2014) Introduction to molecular biology of influenza a viruses. Acta Biochim Pol. 61(3):397–401

  2. 2.

    Schild GC (1979) The influenza virus: Antigenic composition and immune response. Postgrad Med J 55(640):87–97

  3. 3.

    Schrauwen EJ, Fouchier RA (2014) Host adaptation and transmission of influenza A viruses in mammals. Emerg Microb Infect 3(2):e9.

  4. 4.

    Sym D, Patel PN, El-Chaar GM (2009) Seasonal, avian, and novel H1N1 influenza: prevention and treatment modalities. Ann Pharmacother. 43(12):2001–2011.

  5. 5.

    Palache A, Oriol-Mathieu V, Abelin A, Music T (2014) Seasonal influenza vaccine dose distribution in 157 countries (2004–2011). Vaccine 32(48):6369–6376.

  6. 6.

    Yoon SW, Webby RJ, Webster RG (2014) Evolution and ecology of influenza A viruses. Curr Top Microbiol Immunol. 385:359–375.

  7. 7.

    Ping J, Lopes TJ, Nidom CA, Ghedin E, Macken CA, Fitch A et al (2015) Development of high-yield influenza A virus vaccine viruses. Nat Commun. 6:8148.

  8. 8.

    Hashem AM (2015) Prospects of HA-based universal influenza vaccine. Biomed Res Int. 2015:414637. 2015/03/19)

  9. 9.

    Krammer F, Palese P, Steel J (2015) Advances in universal influenza virus vaccine design and antibody mediated therapies based on conserved regions of the hemagglutinin. Curr Top Microbiol Immunol. 386:301–321. 2014/07/11)

  10. 10.

    Laursen NS, Wilson IA (2013) Broadly neutralizing antibodies against influenza viruses. Antiviral Res. 98(3):476–483. 2013/04/16)

  11. 11.

    Schepens B, De Vlieger D, Saelens X (2018) Vaccine options for influenza: thinking small. Curr Opin Immunol. 53:22–29. 2018/04/10)

  12. 12.

    Eichelberger MC, Morens DM, Taubenberger JK (2018) Neuraminidase as an influenza vaccine antigen: a low hanging fruit, ready for picking to improve vaccine effectiveness. Curr Opin Immunol. 53:38–44. 2018/04/21)

  13. 13.

    Xu R, Ekiert DC, Krause JC, Hai R, Crowe JE Jr, Wilson IA (2010) Structural basis of preexisting immunity to the 2009 H1N1 pandemic influenza virus. Science. 328(5976):357–360. 2010/03/27)

  14. 14.

    Wilson IA, Skehel JJ, Wiley DC (1981) Structure of the haemagglutinin membrane glycoprotein of influenza virus at 3 A resolution. Nature 289(5796):366–373 (Epub 1981/01/29)

  15. 15.

    Wu NC, Wilson IA (2017) A perspective on the structural and functional constraints for immune evasion: insights from influenza virus. J Mol Biol. 429(17):2694–2709. 2017/06/27)

  16. 16.

    Wu NC, Wilson IA (2018) Structural insights into the design of novel anti-influenza therapies. Nat Struct Mol Biol. 25(2):115–121. 2018/02/06)

  17. 17.

    Krammer F, Fouchier RAM, Eichelberger MC, Webby RJ, Shaw-Saliba K, Wan H et al (2018) NAction! How can neuraminidase-based immunity contribute to better influenza virus vaccines? MBio. 2018/04/05)

  18. 18.

    Eichelberger MC, Wan H (2015) Influenza neuraminidase as a vaccine antigen. Curr Top Microbiol Immunol. 386:275–299. 2014/07/19)

  19. 19.

    Loyola PK, Campos-Rodriguez R, Bello M, Rojas-Hernandez S, Zimic M, Quiliano M et al (2013) Theoretical analysis of the neuraminidase epitope of the Mexican A H1N1 influenza strain, and experimental studies on its interaction with rabbit and human hosts. Immunol Res. 56(1):44–60. 2013/02/02)

  20. 20.

    Marcelin G, Sandbulte MR, Webby RJ (2012) Contribution of antibody production against neuraminidase to the protection afforded by influenza vaccines. Rev Med Virol. 22(4):267–279. 2012/03/23)

  21. 21.

    López S, Arias C (2010) Influenza A: Biología, vacunas, y origen del virus pandémico A/H1N1. Revista Digital Universitaria

  22. 22.

    Oany AR, Emran AA, Jyoti TP (2014) Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach. Drug Des Devel Ther. 8:1139–1149.

  23. 23.

    Gori A, Longhi R, Peri C, Colombo G (2013) Peptides for immunological purposes: design, strategies and applications. Amino Acids. 45(2):257–268.

  24. 24.

    Pellicci DG, Uldrich AP, Le Nours J, Ross F, Chabrol E, Eckle SB et al (2014) The molecular bases of delta/alphabeta T cell-mediated antigen recognition. J Exp Med. 211(13):2599–2615.

  25. 25.

    Pinilla C, Appel JR, Judkowski V, Houghten RA (2012) Identification of B cell and T cell epitopes using synthetic peptide combinatorial libraries. Curr Protoc Immunol. 2012/11/07)

  26. 26.

    Bao Y, Bolotov P, Dernovoy D, Kiryutin B, Zaslavsky L, Tatusova T et al (2008) The influenza virus resource at the National Center for Biotechnology Information. J Virol. 82(2):596–601.

  27. 27.

    Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5):1792–1797.

  28. 28.

    Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H et al (2007) Clustal W and Clustal X version 2.0. Bioinformatics. 23(21):2947–2948.

  29. 29.

    Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42(Web Server issue):W252–W258.

  30. 30.

    Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics. 22(2):195–201 (Epub 2005/11/23)

  31. 31.

    Kiefer F, Arnold K, Kunzli M, Bordoli L, Schwede T (2009) The SWISS-MODEL repository and associated resources. Nucleic Acids Res. 37(Database issue):D387–D392. 2008/10/22)

  32. 32.

    Guex N, Peitsch MC, Schwede T (2009) Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis. 30(Suppl 1):S162–S173.

  33. 33.

    de Beer TA, Berka K, Thornton JM, Laskowski RA (2014) PDBsum additions. Nucleic Acids Res. 42(Database issue):D292–D296.

  34. 34.

    Macarthur MW, Laskowski RA, Thornton JM (1994) Knowledge-based validation of protein-structure coordinates derived by X-ray crystallography and nmr-spectroscopy. Curr Opin Struc Biol 4(5):731–737.

  35. 35.

    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph. 14(1):33–38 (27–8, Epub 1996/02/01)

  36. 36.

    Carrillo-Vazquez JP, Correa-Basurto J, Garcia-Machorro J, Campos-Rodriguez R, Moreau V, Rosas-Trigueros JL et al (2015) A continuous peptide epitope reacting with pandemic influenza AH1N1 predicted by bioinformatic approaches. J Mol Recognit. 28(9):553–564.

  37. 37.

    Nielsen M, Lund O, Buus S, Lundegaard C (2010) MHC class II epitope predictive algorithms. Immunology. 130(3):319–328.

  38. 38.

    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S et al (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12(5):1007–1017.

  39. 39.

    Nielsen M, Lund O (2009) NN-align An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinform. 10:296.

  40. 40.

    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem. 26(16):1781–1802. 2005/10/14)

  41. 41.

    MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616.

  42. 42.

    Martyna GJ, Tobias DJ, Klein ML (1994) Constant-pressure molecular-dynamics algorithms. J Chem Phys. 101(5):4177–4189.

  43. 43.

    Glykos NM (2006) Software news and updates. Carma: a molecular dynamics analysis program. J Comput Chem. 27(14):1765–1768. 2006/08/19)

  44. 44.

    Contis-Montes de Oca A, Carrasco-Yépez M, Campos-Rodríguez R, Pacheco-Yépez J, Bonilla-Lemus P, Pérez-López J, Rojas-Hernández S (2016) Neutrophils extracellular traps damage Naegleria fowleri trophozoites opsonized with human IgG . Parasite Immunol 38(8):481–495.

  45. 45.

    Kawaoka Y, Neumann G (2012) Influenza viruses: an introduction. Methods Mol Biol. 865:1–9.

  46. 46.

    Morens DM, Halstead SB, Repik PM, Putvatana R, Raybourne N (1985) Simplified plaque reduction neutralization assay for dengue viruses by semimicro methods in BHK-21 cells: comparison of the BHK suspension test with standard plaque reduction neutralization. J Clin Microbiol. 22(2):250–254

  47. 47.

    Chan KH, To KK, Hung IF, Zhang AJ, Chan JF, Cheng VC, Tse H, Che XY, Chen H, Yuen KY (2011) Differences in antibody responses of individuals with natural infection and those vaccinated against pandemic H1N1 2009 influenza. Clin Vaccine Immunol. 18(5):867–873

  48. 48.

    Pinne M, Haake D (2011) Immuno-fluorescence assay of leptospiral surface-exposed proteins. JoVE.

  49. 49.

    Klasse PJ (2014) Neutralization of virus infectivity by antibodies: old problems in new perspectives. Adv Biol.

  50. 50.

    Camacho CJ, Katsumata Y, Ascherman DP (2008) Structural and thermodynamic approach to peptide immunogenicity. PLoS Comput Biol. 4(11):e1000231. 2008/11/22)

  51. 51.

    Altenburg AF, Rimmelzwaan GF, de Vries RD (2015) Virus-specific T cells as correlate of (cross-)protective immunity against influenza. Vaccine. 33(4):500–506.

  52. 52.

    Hufford MM, Kim TS, Sun J, Braciale TJ (2015) The effector T cell response to influenza infection. Curr Top Microbiol Immunol 386:423–455.

  53. 53.

    Hufford MM, Kim TS, Sun J, Braciale TJ (2011) Antiviral CD8+ T cell effector activities in situ are regulated by target cell type. J Exp Med. 208(1):167–180.

  54. 54.

    Chiu C, Ellebedy AH, Wrammert J, Ahmed R (2015) B cell responses to influenza infection and vaccination. Curr Top Microbiol Immunol. 386:381–398.

  55. 55.

    Smith-Garvin JE, Koretzky GA, Jordan MS (2009) T cell activation. Annu Rev Immunol. 27:591–619. 2009/01/10)

  56. 56.

    De Groot AS (2009) Exploring the immunome: a brave new world for human vaccine development. Hum Vaccin. 5(12):790–793 (Epub 2009/12/17)

  57. 57.

    Bienenstock J, McDermott MR (2005) Bronchus- and nasal-associated lymphoid tissues. Immunol Rev 206:22–31.

  58. 58.

    Stavnezer J, Guikema JE, Schrader CE (2008) Mechanism and regulation of class switch recombination. Annu Rev Immunol. 26:261–292. 2008/03/29)

  59. 59.

    Asahi Y, Yoshikawa T, Watanabe I, Iwasaki T, Hasegawa H, Sato Y et al (2002) Protection against influenza virus infection in polymeric Ig receptor knockout mice immunized intranasally with adjuvant-combined vaccines. J Immunol. 168(6):2930–2938 (Epub 2002/03/09)

  60. 60.

    Langley JM, Aoki F, Ward BJ, McGeer A, Angel JB, Stiver G et al (2011) A nasally administered trivalent inactivated influenza vaccine is well tolerated, stimulates both mucosal and systemic immunity, and potentially protects against influenza illness. Vaccine. 29(10):1921–1928.

  61. 61.

    van Riet E, Ainai A, Suzuki T, Hasegawa H (2012) Mucosal IgA responses in influenza virus infections; thoughts for vaccine design. Vaccine. 30(40):5893–5900 (Epub 2012/07/28)

  62. 62.

    Lucchese G, Sinha AA, Kanduc D (2012) How a single amino acid change may alter the immunological information of a peptide. Front Biosci 4:1843–1852

  63. 63.

    Throsby M, van den Brink E, Jongeneelen M, Poon LL, Alard P, Cornelissen L et al (2008) Heterosubtypic neutralizing monoclonal antibodies cross-protective against H5N1 and H1N1 recovered from human IgM+ memory B cells. PLoS One. 3(12):e3942. 2008/12/17)

  64. 64.

    Skountzou I, Koutsonanos DG, Kim JH, Powers R, Satyabhama L, Masseoud F et al (2010) Immunity to pre-1950 H1N1 influenza viruses confers cross-protection against the pandemic swine-origin 2009 A (H1N1) influenza virus. J Immunol. 185(3):1642–1649.

  65. 65.

    Chen JR, Yu YH, Tseng YC, Chiang WL, Chiang MF, Ko YA et al (2014) Vaccination of monoglycosylated hemagglutinin induces cross-strain protection against influenza virus infections. Proc Natl Acad Sci USA. 111(7):2476–2481.

  66. 66.

    Henry Dunand CJ, Leon PE, Kaur K, Tan GS, Zheng NY, Andrews S et al (2015) Preexisting human antibodies neutralize recently emerged H7N9 influenza strains. J Clin Invest. 125(3):1255–1268.

  67. 67.

    Greenbaum JA, Kotturi MF, Kim Y, Oseroff C, Vaughan K, Salimi N et al (2009) Pre-existing immunity against swine-origin H1N1 influenza viruses in the general human population. Proc Natl Acad Sci USA. 106(48):20365–20370. 2009/11/18)

  68. 68.

    Ge X, Tan V, Bollyky PL, Standifer NE, James EA, Kwok WW (2010) Assessment of seasonal influenza A virus-specific CD4 T-cell responses to 2009 pandemic H1N1 swine-origin influenza A virus. J Virol. 84(7):3312–3319. 2010/01/15)

  69. 69.

    Richards KA, Nayak J, Chaves FA, DiPiazza A, Knowlden ZA, Alam S et al (2015) Seasonal influenza can poise hosts for CD4 T-cell immunity to H7N9 Avian influenza. J Infect Dis. 212(1):86–94.

  70. 70.

    De Groot AS, Ardito M, McClaine EM, Moise L, Martin WD (2009) Immunoinformatic comparison of T-cell epitopes contained in novel swine-origin influenza A (H1N1) virus with epitopes in 2008–2009 conventional influenza vaccine. Vaccine. 27(42):5740–5747. 2009/08/08)

  71. 71.

    Garcia-Machorro J, Lopez-Gonzalez M, Barrios-Rojas O, Fernandez-Pomares C, Sandoval-Montes C, Santos-Argumedo L et al (2013) DENV-2 subunit proteins fused to CR2 receptor-binding domain (P28)-induces specific and neutralizing antibodies to the Dengue virus in mice. Hum Vaccines Immunother. 9(11):2326–2335

  72. 72.

    Fox A, le Mai Q, le Thanh T, Wolbers M, Le Khanh Hang N, Thai PQ et al (2015) Hemagglutination inhibiting antibodies and protection against seasonal and pandemic influenza infection. J Infect. 70(2):187–196.

Download references


The authors were supported by ICyTDF (Dr. Campos), the Instituto Politécnico Nacional (IPN), BEIFI, COFAA-IPN, and grants CB-254600, PDCPN-782 (CONACYT), 241339 (CONACYT), and SIP-IPN (SIP20171881).

Author information

Correspondence to Jazmín García-Machorro or José Correa-Basurto.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: William G Dundon.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 864 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ramírez-Salinas, G.L., García-Machorro, J., Rojas-Hernández, S. et al. Bioinformatics design and experimental validation of influenza A virus multi-epitopes that induce neutralizing antibodies. Arch Virol (2020).

Download citation