(2021) In silico analysis and prediction of immunogenic epitopes for pre-erythrocytic proteins of the deadly Plasmodium falciparum. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. p. 104985. ISSN 1567-7257 (Electronic) 1567-1348 (Linking)
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Abstract
Malaria is the deadliest parasitic disease in tropical and subtropical areas around the world, with considerable morbidity and mortality, particularly due to the life-threatening Plasmodium falciparum. The present in silico investigation was performed to reveal the biophysical characteristics and immunogenic epitopes of the six pre-erythrocytic proteins of the P. falciparum using comprehensive immunoinformatics approaches. For this aim, different web servers were employed to predict subcellular localization, antigenicity, allergenicity, solubility, physico-chemical properties, post-translational modification sites (PTMs), the presence of signal peptide and transmembrane domains. Moreover, the secondary and tertiary structures of the proteins were revealed followed by refinement and validations. Finally, NetCTL server was used to predict cytotoxic T-lymphocyte (CTL) epitopes, followed by subsequent screening in terms of antigenicity and immunogenicity. Also, IEDB server was utilized to predict helper T-lymphocyte (HTL) epitopes, followed by screening regarding interferon gamma induction and population coverage. These proteins showed appropriate antigenicity, abundant PTMs as well as many CTL and HTL epitopes, which could be directed for future vaccination studies in the context of multi-epitope vaccine design.
Item Type: | Article | ||||||||||||||
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Keywords: | Immunogenic epitopes In silico analysis Malaria Plasmodium falciparum | ||||||||||||||
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Page Range: | p. 104985 | ||||||||||||||
Journal or Publication Title: | Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases | ||||||||||||||
Journal Index: | Pubmed | ||||||||||||||
Identification Number: | https://doi.org/10.1016/j.meegid.2021.104985 | ||||||||||||||
ISSN: | 1567-7257 (Electronic) 1567-1348 (Linking) | ||||||||||||||
Depositing User: | مهندس مهدی شریفی | ||||||||||||||
URI: | http://eprints.medilam.ac.ir/id/eprint/3474 |
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