Epitope Predictions

doi:10.1016/B978-0-12-809633-8.20248-3

Reference Module in Life Sciences

Encyclopedia of Bioinformatics and Computational Biology

Volume 2, 2019, Pages 952-971

https://doi.org/10.1016/B978-0-12-809633-8.20248-3 Get rights and content

Abstract

Epitopes or antigenic determinants are regions of proteins that can trigger a cellular immune response mediated by T or B cells. T cell epitopes are usually protein antigen-derived peptides presented by MHC molecules on antigen-presenting cells and recognized by T-cell receptors. B cell epitopes are either peptides or protein surface residues that bind to an antibody. Since epitopes are central to vaccine design, infectious disease prevention, disease diagnosis and treatment we discuss and analyze computational approaches to predict epitopes. Epitope prediction reduces time and cost to arrive from a protein sequence at a vaccine, diagnostic or therapeutic peptide or protein.

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Cited by (5)

Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains
2022, Informatics in Medicine Unlocked
Citation Excerpt :
The epitopes were predicted, and the conserved epitopes were used for the construction of the vaccine. Epitopes are referred to as antigenic determinants encoding the protein region capable of stimulating cell-mediated immunity and are the hallmark of vaccine design [58]. Antigen definite T-cell is cell-mediated, Cell-mediated, and innate immunity is important in the clearance of invasive organisms such as SARS-CoV-2.
The discovery of a new SARS-CoV-2 virus strain in South Africa presents a major public health threat, therefore contributing to increased infections and transmission rates during the second wave of the global pandemic. This study lays the groundwork for the development of a novel subunit vaccine candidate from the circulating strains of South African SARS-CoV-2 and provides an understanding of the molecular epidemiological trend of the circulating strains. A total of 475 whole-genome nucleotide sequences from South Africa submitted between December 1, 2020 and February 15, 2021 available at the GISAID database were retrieved based on its size, coverage level and hosts. To obtain the distribution of the clades and lineages of South African SARS-CoV-2 circulating strains, the metadata of the sequence retrieved were subjected to an epidemiological analysis. There was a prediction of the cytotoxic T lymphocytes (CTL), Helper T cells (HTL) and B-cell epitopes. Furthermore, there was allergenicity, antigenicity and toxicity predictions on the epitopes. The analysis of the physicochemical properties of the vaccine construct was performed; the secondary structure, tertiary structure and B-cell 3D conformational structure of the vaccine construct were predicted. Also, molecular binding simulations and dynamics simulations were adopted in the prediction of the vaccine construct's stability and binding affinity with TLRs. Result obtained from the metadata analysis indicated lineage B.1.351 to be in higher circulation among various circulating strains of SARS-CoV-2 in South Africa and GH has the highest number of circulating clades. The construct of the novel vaccine was antigenic, non-allergenic and non-toxic. The Instability index (II) score and aliphatic index were estimated as 41.74 and 78.72 respectively. The computed half-life in mammalian reticulocytes was 4.4 h in vitro, for yeast and in E. coli was >20 h and >10 h in vivo respectively. The grand average of hydropathicity (GRAVY) score is estimated to be −0.129, signifying the hydrophilic nature of the protein. The molecular docking indicates that the vaccine construct has a high binding affinity towards the TLRs with TLR 3 having the highest binding energy (−1203.2 kcal/mol) and TLR 9 with the lowest (−1559.5 kcal/mol). These results show that the vaccine construct is promising and should be evaluated using animal model.
Design and validation of a multi-epitope vaccine candidate against Acinetobacter baumannii using advanced computational methods
2023, Research Square
HPV and molecular mimicry in systemic lupus erythematosus and an impact of compiling B-cell epitopes and MHC-class II binding profiles with in silico evidence
2023, Journal of Biomolecular Structure and Dynamics
Immunoinformatic Approach to Contrive a Next Generation Multi-Epitope Vaccine Against Achromobacter xylosoxidans Infections
2022, Frontiers in Medicine
Clostridioides Difficile Toxin B (tcdB) Peptide Mimotope Antibody-capture Enzyme Immunoassay in the Detection of Colorectal Cancer
2021, Research Square

Roman Kogay is an undergraduate student at School of Science and Technology, Department of Biology, Nazarbayev University. His research interests include application of bioinformatics in metabolomics, genomics, proteomics and biomedical sciences. After graduation he hopes to pursue a doctoral degree.

Christian Schönbach is Professor at Graduate School of Medical Sciences, International Research Center for Medical Sciences, Kumamoto University. Prior to joining Kumamoto University he was Professor at School of Science and Technology, Department of Biology, Nazarbayev University (2013–2016). His research and teaching interests revolve around bioinformatics, genomics and immunology. Since 2010 he is serving the bioinformatics community of Asia-Pacific Bioinformatics Network (APBioNet) in various leadership roles.

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