Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/205324
Title: Computational Prediction of Trypanosoma cruzi Epitopes Toward the Generation of an Epitope-Based Vaccine Against Chagas Disease
Author: Ros Lucas, Albert
Rioja Soto, David
Gascon, Joaquim
Alonso Padilla, Julio
Keywords: Malaltia de Chagas
Vacunes
Chagas' disease
Vaccines
Issue Date: 1-Jun-2023
Publisher: Springer Nature
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. © 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Note: https://doi.org/10.1007/978-1-0716-3239-0_32
It is part of: Methods In Molecular Biology, 2023, vol. 2673, p. 487-504
URI: http://hdl.handle.net/2445/205324
Related resource: https://doi.org/10.1007/978-1-0716-3239-0_32
ISSN: 1064-3745
Appears in Collections:Llibres / Capítols de llibre (ISGlobal)

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