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      In silico construction of a multiepitope Zika virus vaccine using immunoinformatics tools

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          Abstract

          Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine’s average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

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          Protein Identification and Analysis Tools on the ExPASy Server

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            Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial

            Neoantigens, which are derived from tumour-specific protein-coding mutations, are exempt from central tolerance, can generate robust immune responses1,2 and can function as bona fide antigens that facilitate tumour rejection3. Here we demonstrate that a strategy that uses multi-epitope, personalized neoantigen vaccination, which has previously been tested in patients with high-risk melanoma4-6, is feasible for tumours such as glioblastoma, which typically have a relatively low mutation load1,7 and an immunologically 'cold' tumour microenvironment8. We used personalized neoantigen-targeting vaccines to immunize patients newly diagnosed with glioblastoma following surgical resection and conventional radiotherapy in a phase I/Ib study. Patients who did not receive dexamethasone-a highly potent corticosteroid that is frequently prescribed to treat cerebral oedema in patients with glioblastoma-generated circulating polyfunctional neoantigen-specific CD4+ and CD8+ T cell responses that were enriched in a memory phenotype and showed an increase in the number of tumour-infiltrating T cells. Using single-cell T cell receptor analysis, we provide evidence that neoantigen-specific T cells from the peripheral blood can migrate into an intracranial glioblastoma tumour. Neoantigen-targeting vaccines thus have the potential to favourably alter the immune milieu of glioblastoma.
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              ElliPro: a new structure-based tool for the prediction of antibody epitopes

              Background Reliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes. Results Here we present ElliPro, a web-tool that implements Thornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at . Conclusion The results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.
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                Author and article information

                Contributors
                fonseca.simone@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 January 2022
                7 January 2022
                2022
                : 12
                : 53
                Affiliations
                [1 ]GRID grid.411195.9, ISNI 0000 0001 2192 5801, Department of Bioscience and Technology, Institute of Tropical Pathology and Public Health, , Federal University of Goiás, ; Rua 235 s/n, sala 335, Setor Universitário, Goiânia, GO 74605-050 Brazil
                [2 ]GRID grid.412263.0, ISNI 0000 0001 2355 1516, Departament of Master in Environmental Sciences and Health, School of Medical, Pharmaceutical and Biomedical Sciences, , Pontifical Catholic University of Goiás, ; Goiânia, Brazil
                [3 ]GRID grid.7632.0, ISNI 0000 0001 2238 5157, Department of Cell Biology, , University of Brasília, ; Brasília, Brazil
                [4 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Heart Institute (InCor), School of Medicine, , University of São Paulo, ; São Paulo, Brazil
                [5 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Institute for Investigation in Immunology (iii) - National Institute of Science and Technology (INCT), ; São Paulo, Brazil
                Article
                3990
                10.1038/s41598-021-03990-6
                8741764
                34997041
                fc96c5c2-f1c6-4dcf-baf4-b8277eb4b4e7
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 April 2021
                : 1 December 2021
                Funding
                Funded by: Conselho Nacional de Pesquisa (CNPq)
                Award ID: 134145/2018-6
                Award Recipient :
                Funded by: Fundação de Amparo à Pesquisa do Distrito Federal (FAPDF)
                Award ID: 0193.000417/2016
                Award ID: 0193.000417/2016
                Award Recipient :
                Funded by: Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG)
                Award ID: 201710267001260
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                peptide vaccines,infectious diseases,immunology
                Uncategorized
                peptide vaccines, infectious diseases, immunology

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