Skip to main content
SpringerLink
Log in

Computational Prediction of Trypanosoma cruzi Epitopes Toward the Generation of an Epitope-Based Vaccine Against Chagas Disease

  • Protocol
  • First Online:
Computational Vaccine Design

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

Abstract

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, is considered a Neglected Tropical Disease. Limited investment is assigned to its study and control, even though it is one of the most prevalent parasitic infections worldwide. An innovative vaccination strategy involving an epitope-based vaccine that displays multiple immune determinants originating from different antigens could counteract the high biological complexity of the parasite and lead to a wide and protective immune response. In this chapter, we describe a computational reverse vaccinology pipeline applied to identify the most promising peptide sequences from T. cruzi proteins, prioritizing evolutionary conserved sequences, to finally select a list of T and B cell epitope candidates to be further tested in an experimental setting.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Beaumier CM, Gillespie PM, Strych U et al (2016) Status of vaccine research and development of vaccines for Chagas disease. Vaccine 34:2996–3000. https://doi.org/10.1016/j.vaccine.2016.03.074

    Article  PubMed  Google Scholar 

  2. Pinazo M-J, Gascon J (2015) Chagas disease: from Latin America to the world. Rep Parasitol 4:7–14. https://doi.org/10.2147/RIP.S57144

    Article  Google Scholar 

  3. Gascon J, Bern C, Pinazo M-J (2010) Chagas disease in Spain, the United States and other non-endemic countries. Acta Trop 115:22–27. https://doi.org/10.1016/j.actatropica.2009.07.019

    Article  PubMed  Google Scholar 

  4. Aldasoro E, Posada E, Requena-Méndez A et al (2018) What to expect and when: benznidazole toxicity in chronic Chagas’ disease treatment. J Antimicrob Chemother 73:1060–1067. https://doi.org/10.1093/jac/dkx516

    Article  CAS  PubMed  Google Scholar 

  5. Jackson Y, Alirol E, Getaz L et al (2010) Tolerance and safety of nifurtimox in patients with chronic Chagas disease. Clin Infect Dis 51:e69–e75. https://doi.org/10.1086/656917

    Article  CAS  PubMed  Google Scholar 

  6. Teh-Poot C, Dumonteil E (2019) Mining Trypanosoma cruzi genome sequences for antigen discovery and vaccine development. In: Gómez KA, Buscaglia CA (eds) T. cruzi infection. Springer New York, New York, pp 23–34

    Chapter  Google Scholar 

  7. Vita R, Mahajan S, Overton JA et al (2019) The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 47:D339–D343. https://doi.org/10.1093/nar/gky1006

    Article  CAS  PubMed  Google Scholar 

  8. Galanis KA, Nastou KC, Papandreou NC et al (2021) Linear B-cell epitope prediction for in silico vaccine design: a performance review of methods available via command-line interface. Int J Mol Sci 22:3210. https://doi.org/10.3390/ijms22063210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sullivan NL, Eickhoff CS, Sagartz J, Hoft DF (2015) Deficiency of antigen-specific B cells results in decreased Trypanosoma cruzi systemic but not mucosal immunity due to CD8 T cell exhaustion. J Immunol 194:1806–1818. https://doi.org/10.4049/jimmunol.1303163

    Article  CAS  PubMed  Google Scholar 

  10. Buschiazzo A, Muiá R, Larrieux N et al (2012) Trypanosoma cruzi trans-sialidase in complex with a neutralizing antibody: structure/function studies towards the rational design of inhibitors. PLoS Pathog 8:e1002474. https://doi.org/10.1371/journal.ppat.1002474

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Varadi M, Anyango S, Deshpande M et al (2022) AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439–D444. https://doi.org/10.1093/nar/gkab1061

    Article  CAS  PubMed  Google Scholar 

  12. Aslett M, Aurrecoechea C, Berriman M et al (2010) TriTrypDB: a functional genomic resource for the Trypanosomatidae. Nucleic Acids Res 38:D457–D462. https://doi.org/10.1093/nar/gkp851

    Article  CAS  PubMed  Google Scholar 

  13. Amos B, Aurrecoechea C, Barba M et al (2022) VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center. Nucleic Acids Res 50:D898–D911. https://doi.org/10.1093/nar/gkab929

    Article  CAS  PubMed  Google Scholar 

  14. The NIH HMP Working Group, Peterson J, Garges S et al (2009) The NIH human microbiome project. Genome Res 19:2317–2323. https://doi.org/10.1101/gr.096651.109

    Article  CAS  PubMed Central  Google Scholar 

  15. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659. https://doi.org/10.1093/bioinformatics/btl158

    Article  CAS  PubMed  Google Scholar 

  16. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797. https://doi.org/10.1093/nar/gkh340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nielsen M, Lundegaard C, Lund O, Keşmir C (2005) The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57:33–41. https://doi.org/10.1007/s00251-005-0781-7

    Article  CAS  PubMed  Google Scholar 

  18. Besser H, Louzoun Y (2018) Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide. Immunogenetics 70:419–428. https://doi.org/10.1007/s00251-018-1054-6

    Article  CAS  PubMed  Google Scholar 

  19. Reynisson B, Alvarez B, Paul S et al (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 48:W449–W454. https://doi.org/10.1093/nar/gkaa379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Teufel F, Almagro Armenteros JJ, Johansen AR et al (2022) SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 40:1023–1025. https://doi.org/10.1038/s41587-021-01156-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gíslason MH, Nielsen H, Almagro Armenteros JJ, Johansen AR (2021) Prediction of GPI-anchored proteins with pointer neural networks. Curr Res Biotechnol 3:6–13. https://doi.org/10.1016/j.crbiot.2021.01.001

    Article  CAS  Google Scholar 

  22. 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 45:W24–W29. https://doi.org/10.1093/nar/gkx346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hubbard SJ, Thornton JM (1993) ‘NACCESS’, computer program. Department of Biochemistry and Molecular Biology, University College, London

    Google Scholar 

  24. Altschul S (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402. https://doi.org/10.1093/nar/25.17.3389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Resource Coordinators NCBI, Agarwala R, Barrett T et al (2018) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 46:D8–D13. https://doi.org/10.1093/nar/gkx1095

    Article  CAS  Google Scholar 

  26. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x

    Article  Google Scholar 

  27. Alonso-Padilla J, Lafuente EM, Reche PA (2017) Computer-aided design of an epitope-based vaccine against Epstein-Barr virus. J Immunol Res 2017:1–15. https://doi.org/10.1155/2017/9363750

    Article  CAS  Google Scholar 

  28. Stewart JJ, Lee CY, Ibrahim S et al (1997) A Shannon entropy analysis of immunoglobulin and T cell receptor. Mol Immunol 34:1067–1082. https://doi.org/10.1016/S0161-5890(97)00130-2

    Article  CAS  PubMed  Google Scholar 

  29. Weiskopf D, Angelo MA, de Azeredo EL et al (2013) Comprehensive analysis of dengue virus-specific responses supports an HLA-linked protective role for CD8+ T cells. Proc Natl Acad Sci U S A 110:E2046–E2053. https://doi.org/10.1073/pnas.1305227110

    Article  PubMed  PubMed Central  Google Scholar 

  30. Greenbaum J, Sidney J, Chung J et al (2011) Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes. Immunogenetics 63:325–335. https://doi.org/10.1007/s00251-011-0513-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tunyasuvunakool K, Adler J, Wu Z et al (2021) Highly accurate protein structure prediction for the human proteome. Nature 596:590–596. https://doi.org/10.1038/s41586-021-03828-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Petersen B, Petersen TN, Andersen P et al (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol 9:51. https://doi.org/10.1186/1472-6807-9-51

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. This research was also supported by CIBER—Consorcio Centro de Investigación Biomédica en Red- (CB 2021), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and Unión Europea—NextGenerationEU. A.R-L., J.G. and J.A-P. belong to the AGAUR Grup de Recerca Consolidat 2021SGR01562.

Author information

Authors and Affiliations

  1. Barcelona Institute for Global Health (ISGlobal), Hospital Clinic – University of Barcelona, Barcelona, Spain

    Albert Ros-Lucas, David Rioja-Soto, Joaquim Gascón & Julio Alonso-Padilla

  2. CIBERINFEC, ISCIII—CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain

    Albert Ros-Lucas, Joaquim Gascón & Julio Alonso-Padilla

Authors
  1. Albert Ros-Lucas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Rioja-Soto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joaquim Gascón
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julio Alonso-Padilla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Albert Ros-Lucas or Julio Alonso-Padilla .

Editor information

Editors and Affiliations

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

    Pedro A. Reche

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Ros-Lucas, A., Rioja-Soto, D., Gascón, J., Alonso-Padilla, J. (2023). Computational Prediction of Trypanosoma cruzi Epitopes Toward the Generation of an Epitope-Based Vaccine Against Chagas Disease. 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_32

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3239-0_32

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3238-3

  • Online ISBN: 978-1-0716-3239-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation