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Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design

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Computational Vaccine Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2673))

Abstract

Reverse vaccinology (RV) consists in the identification of potentially protective antigens expressed by any organism starting from genomic information and derived from in silico analysis, with the aim of promoting the discovery of new candidate vaccines against different types of pathogens. This approach makes use of bioinformatics techniques to screen the whole genomic sequence of a specific pathogen for the identification of the epitopes that could elicit the best immune response. The use of in silico techniques allows to reduce dramatically both the time and cost required for the identification of a potential vaccine, also facilitating the laborious process of selection of those antigens that, with a traditional approach, would be completely impossible to detect or culture. RV methodologies have been successfully applied for the identification of new vaccines against serogroup B meningococcus (MenB), Bacillus anthracis, Streptococcus pneumonia, Staphylococcus aureus, Chlamydia pneumoniae, Porphyromonas gingivalis, Edwardsiella tarda, and Mycobacterium tuberculosis. As a case of study, we will go in depth into the application of RV techniques on Influenza A virus.

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Notes

  1. 1.

    http://www.violinet.org/vaxign/

  2. 2.

    https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastHome

  3. 3.

    http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html

References

  1. Rappuoli R (2000) Reverse vaccinology. Curr Opin Microbiol [Internet] 3(5):445–450. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1369527400001193

    Article  CAS  PubMed  Google Scholar 

  2. De Sousa KP, Doolan DL (2016) Immunomics: a 21st century approach to vaccine development for complex pathogens. Parasitology [Internet] 143(2):236–244. Available from: https://www.cambridge.org/core/product/identifier/S0031182015001079/type/journal_article

    Article  PubMed  Google Scholar 

  3. Adu-Bobie J (2003) Two years into reverse vaccinology. Vaccine [Internet] 21(7–8):605–610. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0264410X02005662

    Article  PubMed  Google Scholar 

  4. Black S, Bloom DE, Kaslow DC, Pecetta S, Rappuoli R (2020) Transforming vaccine development. Semin Immunol [Internet] 50:101413. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1044532320300294

    Article  CAS  PubMed  Google Scholar 

  5. Palumbo E, Fiaschi L, Brunelli B, Marchi S, Savino S, Pizza M (2012) Antigen identification starting from the genome: a “reverse vaccinology” approach applied to MenB, pp 361–403. Available from: http://link.springer.com/10.1007/978-1-61779-346-2_21

    Google Scholar 

  6. Masignani V, Pizza M, Moxon ER (2019. Available from: https://www.frontiersin.org/article/10.3389/fimmu.2019.00751/full) The development of a vaccine against meningococcus B using reverse vaccinology. Front Immunol [Internet] 10

  7. Dalsass M, Brozzi A, Medini D, Rappuoli R (2019. Available from: https://www.frontiersin.org/article/10.3389/fimmu.2019.00113/full) Comparison of open-source reverse vaccinology programs for bacterial vaccine antigen discovery. Front Immunol [Internet] 10

  8. Heinson AI, Woelk CH, Newell M-L (2015) The promise of reverse vaccinology. Int Health [Internet] 7(2):85–89. Available from: https://academic.oup.com/inthealth/article-lookup/doi/10.1093/inthealth/ihv002

    Article  PubMed  Google Scholar 

  9. Vivona S, Bernante F, Filippini F (2006) NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol [Internet] 6(1):35. Available from: https://bmcbiotechnol.biomedcentral.com/articles/10.1186/1472-6750-6-35

    Article  PubMed  Google Scholar 

  10. Heinson A, Gunawardana Y, Moesker B, Hume C, Vataga E, Hall Y et al (2017) Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. Int J Mol Sci [Internet] 18(2):312. Available from: http://www.mdpi.com/1422-0067/18/2/312

    Article  PubMed  Google Scholar 

  11. Russo G, Pennisi M, Fichera E, Motta S, Raciti G, Viceconti M et al (2020) In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform. BMC Bioinformatics [Internet] 21(17):1–16. Available from: https://doi.org/10.1186/s12859-020-03872-0

    Google Scholar 

  12. Russo G, Di Salvatore V, Sgroi G, Parasiliti Palumbo GA, Reche PA, Pappalardo F (2022) A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets. Brief Bioinform [Internet]. [cited 2022 Apr 24];23(1). Available from: https://pubmed.ncbi.nlm.nih.gov/34607353/

  13. Giulia R, Elena C, Avisa M, Di Salvatore Valentina PF (2022) Beyond the state of the art of reverse vaccinology: predicting vaccine e. Res Sq Prepr

    Google Scholar 

  14. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol [Internet] 7(1):539. Available from: https://onlinelibrary.wiley.com/doi/10.1038/msb.2011.75

    Article  PubMed  Google Scholar 

  15. Procter JB, Carstairs GM, Soares B, Mourão K, Ofoegbu TC, Barton D et al (2021) Alignment of biological sequences with Jalview, vol 2231, pp 203–224. Available from: http://link.springer.com/10.1007/978-1-0716-1036-7_13

    Google Scholar 

  16. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M (2007) Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics [Internet] 8(1):424. Available from: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-424

    Article  PubMed  Google Scholar 

  17. Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M (2020) NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res [Internet] 48(W1):W449–W454. Available from: https://academic.oup.com/nar/article/48/W1/W449/5837056

    Article  CAS  PubMed  Google Scholar 

  18. Jespersen MC, Peters B, Nielsen M, Marcatili P (2017) BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res [Internet] 45(W1):W24–W29. Available from: https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx346

    Article  CAS  PubMed  Google Scholar 

  19. Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics [Internet] 8(1):4. Available from: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-4

    Article  PubMed  Google Scholar 

  20. Dimitrov I, Bangov I, Flower DR, Doytchinova I (2014) AllerTOP v.2—a server for in silico prediction of allergens. J Mol Model [Internet] 20(6):2278. Available from: http://link.springer.com/10.1007/s00894-014-2278-5

    Article  PubMed  Google Scholar 

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Acknowledgments

The authors acknowledge partial support from University of Catania, internal grants.

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Authors and Affiliations

  1. Department of Health and Drug Sciences, Università degli Studi di Catania (IT), Catania, Italy

    Valentina Di Salvatore, Giulia Russo & Francesco Pappalardo

Authors
  1. Valentina Di Salvatore
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  2. Giulia Russo
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  3. Francesco Pappalardo
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Corresponding author

Correspondence to Francesco Pappalardo .

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Editors and Affiliations

  1. School of Medicine, Department of Immunology, Complutense University of Madrid, Madrid, Spain

    Pedro A. Reche

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Di Salvatore, V., Russo, G., Pappalardo, F. (2023). Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design. In: Reche, P.A. (eds) Computational Vaccine Design. Methods in Molecular Biology, vol 2673. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3239-0_27

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  • DOI: https://doi.org/10.1007/978-1-0716-3239-0_27

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3238-3

  • Online ISBN: 978-1-0716-3239-0

  • eBook Packages: Springer Protocols

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