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- W4375859901 abstract "Monkeypox is a zoonotic virus that has recently affected different countries worldwide. On July 23, 2022, the WHO declared the outbreak of monkeypox as a public health emergency of international concern. Surveillance studies conducted in Central Africa in the 1980s and later during outbreaks in the same region showed smallpox vaccines to be clinically somewhat effective against Monkeypox virus. However, there is no specific vaccine against this virus. This research used bioinformatics techniques to establish a novel multi-epitope vaccine candidate against Monkeypox that can induce a strong immune response. Five well-known antigenic proteins (E8L, A30L, A35R, A29L, and B21R) of the virus were picked and assessed as possible immunogenic peptides. Two suitable peptide candidates were selected according to bio-informatics analysis. Based upon in silico evaluation, two multi-epitope vaccine candidates (ALALAR and ALAL) were built with rich-epitope domains consisting of high-ranking T and B-cell epitopes. After predicting and evaluating the 3D structure of the protein candidates, the most efficient 3D models were considered for docking studies with Toll-like receptor 4 (TLR4) and the HLA-A * 11:01, HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*07:02, HLA-A*15:01, HLA-A*30:01 receptors. Subsequently, molecular dynamics (MD) simulation of up to 150 nanoseconds was employed to assess the durability of the interaction of the vaccine candidates with immune receptors. MD studies showed that M5-HLA-A*11:01, ALAL-TLR4, and ALALAR-TLR4 complexes were stable during simulation. Analysis of the in silico outcomes indicates that the M5 peptide and ALAL and ALALAR proteins may be suitable vaccine candidates against the Monkeypox virus.Communicated by Ramaswamy H. Sarma." @default.
- W4375859901 created "2023-05-10" @default.
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- W4375859901 date "2023-05-08" @default.
- W4375859901 modified "2023-09-25" @default.
- W4375859901 title "Design peptide and multi-epitope protein vaccine candidates against monkeypox virus using reverse vaccinology approach: an in-silico study" @default.
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- W4375859901 doi "https://doi.org/10.1080/07391102.2023.2201850" @default.
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- W4375859901 hasPublicationYear "2023" @default.
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