Please use this identifier to cite or link to this item: http://monografias.ufrn.br/handle/123456789/11371
Title: Identification of Promiscuous T cell Epitopes on Mayaro virus structural proteins using immunoinformatics, molecular modeling, and QM:MM approaches
Authors: Silva, Maria Karolaynne
Keywords: Mayaro virus;Immunoinformatics;Epitope prediction;MHC Class I and II;TCD8+;TCD4+
Issue Date: 10-Mar-2021
Publisher: Universidade Federal do Rio Grande do Norte
Citation: SILVA, Maria Karolaynne da. Identification of Promiscuous T cell Epitopes on Mayaro virus structural proteins using immunoinformatics, molecular modeling, and QM:MM approaches. 2021. 30 f. Trabalho de Conclusão de Curso (Graduação em Farmácia) - Departamento de Farmácia, Universidade Federal do Rio Grande do Norte, Natal, 2021.
Portuguese Abstract: The Mayaro virus (MAYV) belongs to genus Alphavirus (family Togaviridae) and has been reported in several countries, especially in tropical regions of America. Due to its outbreaks and potential lack of medication, an effective vaccine formulation is strongly required. This study aimed to predict promiscuous T cell epitopes from structural polyproteins of MAYV using an immunoinformatics approach. For this purpose, consensus sequences were used to identify short protein sequences capable of binding to MHC class I and class II alleles. Our analysis pointed out 4 MHC-I/TCD8+ and 21 MHC-II/TCD4+ epitopes on capside (1;3), E1 (2;5), E2 (1;10), E3 (0;2), and 6K (0;1) proteins. These predicted epitopes were characterized by high antigenicity, immunogenicity, conservancy, non-allergenic, non-toxic, and good population coverage rate values for North and South American geographical areas. Afterwards, we used the crystal structure of human toll-like receptor 3 (TLR3) ectodomain as a template to predict, through docking essays, the placement of a vaccine prototype at the TLR3 receptor binding site. Finally, classical and quantum mechanics/molecular mechanics (QM:MM) computations were employed to improve the quality of docking calculations, with the QM part of the simulations being accomplished by using the density functional theory (DFT) formalism. These results provide important insights into the advancement of diagnostic platforms, the development of vaccines, and immunotherapeutic interventions.
URI: http://monografias.ufrn.br/handle/123456789/11371
Other Identifiers: 2016080660
Appears in Collections:Farmácia

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