rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Reverse vaccinology
    sub title: concept, methods and advancement
  name:
    name part: Das, Jayashankar
    role:
      role term: editor.
  name:
    name part: Dave, Sushma
    role:
      role term: editor.
  name:
    name part: Soares, Siomar
    role:
      role term: editor.
  name:
    name part: Tiwari, Sandeep
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Reverse Vaccinology: Concept, Methods and Advancement presents the development strategy of new vaccines through genome sequencing bioinformatics analysis. Reverse vaccinology promises to revolutionize vaccine development, especially for pathogens to which the classical applications of Pasteur's principles have failed, and it is explained in detail in this book.The book is split into three sections: the first, Concept, brings the basis of reverse vaccinology, vaccine antigen discovery, and subunit vaccine; the second, Tools and Methods, describes immunoinformatic, proteomics for epitope-vaccine design, data bases, network analysis, machine learning, and NGS driven antigen screening technology; and the last one, Disease Case Study, discusses real-world examples in the development of new vaccines for diverse diseases.It is a valuable resource for bioinformaticians, researchers, students, and member of the biomedical and medical fields who want to learn more about a new and agile process for the development of new vaccines
  table of contents: Front Cover -- Reverse Vaccinology -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- 1 Concept -- 1 Fundamentals of reverse vaccinology: history and advantages over the discovery of conventional vaccine candidates -- 1.1 Immune system -- 1.2 Classical vaccinology -- 1.2.1 Vaccine history and development -- 1.2.2 Classic vaccines used today -- 1.2.3 Administration routes -- 1.2.3.1 Oral route -- 1.2.3.2 Parenteral route -- 1.3 Omics and reverse vaccinology -- 1.3.1 Omics -- 1.3.2 Reverse vaccinology -- 1.3.3 Advantages and disadvantages of reverse vaccinology -- 1.3.4 Aplications of reverse vaccinology -- 1.3.5 Methodologies used in the reverse vaccinology process -- 1.4 Conclusion -- References -- 2 Vaccine antigen discovery: a breakthrough in genomic era -- 2.1 Introduction -- 2.2 Conventional vaccines to biotechnological vaccines -- 2.3 Antigen discovery by reverse vaccinology -- 2.4 Conclusion -- References -- 3 Development of subunit vaccine: A step forward toward cost-effective technology of vaccine candidate discovery -- 3.1 Introduction -- 3.2 Importance of subunit vaccines in vaccine discovery -- 3.3 Advantages of subunit vaccines over conventional treatments -- 3.4 Methods for discovering antigenic determinants -- 3.4.1 Proteomics: analyzing the pathogen's protein composition -- 3.4.2 Genetics: identifying specific genes responsible for antigenic determinants -- 3.4.3 Immunological assays: testing the immune response to pathogen fragments -- 3.5 Development and testing of subunit vaccines -- 3.5.1 Selection of antigenic determinants for vaccine formulation -- 3.5.2 Evaluation of immunogenicity and safety of selected determinants -- 3.5.3 Incorporation of determinants into subunit vaccine design -- 3.6 Potential benefits of subunit vaccines.
  table of contents: 3.6.1 Generation of large quantities for widespread distribution -- 3.6.2 Demonstrated effectiveness and safety -- 3.6.3 Potential for reducing the global burden of infectious diseases -- 3.7 Conclusion -- References -- 2 Tools and methods -- 4 Machine learning approach for vaccine development-fundamentals -- 4.1 Introduction -- 4.2 Classification of machine learning algorithms -- 4.2.1 Supervised machine learning -- 4.2.2 Unsupervised machine learning -- 4.3 Clustering -- 4.4 Dimensionality reduction -- 4.5 Regression -- 4.6 Classification -- 4.6.1 Principal component analysis -- 4.6.2 Decision tree -- 4.6.3 Bayesian (Naive Bayesian) classification -- 4.6.4 Support vector machine -- 4.6.5 Artificial neural network self-organizing maps -- 4.6.6 Deep learning algorithms -- 4.7 Conclusion -- References -- 5 Immunoinformatics: an interdisciplinary technique for designing and engineering vaccine antigen -- 5.1 Introduction -- 5.2 Development of a multiepitope vaccine -- 5.2.1 In silico prediction -- 5.2.2 Protein sequence retrieval -- 5.2.3 Epitope prediction of B cell -- 5.2.4 Epitope prediction of T cell -- 5.3 Evaluation of antigenic, allergenic, immunogenicity, and toxicity -- 5.4 Population coverage analysis -- 5.5 Molecular docking -- 5.6 Molecular dynamic prediction -- 5.7 Construction of the peptide vaccine -- 5.8 Future challenges and conclusion -- References -- 6 Proteomics for epitope-based vaccine design -- 6.1 Introduction -- 6.2 Western blotting and two-dimensional gel electrophoresis -- 6.3 Enzyme-linked immunosorbent assay -- 6.4 Phage display -- 6.5 Immunoprecipitation and epitope extraction -- 6.6 Mass spectrometry in proteomics -- 6.7 Proteomics workflows -- 6.8 Posttranslational modifications -- 6.9 Proteogenomics -- 6.10 Conclusion -- References -- 7 Databases and web server for conducting reverse vaccinology -- 7.1 Introduction.
  table of contents: 7.2 Online tools and web servers -- 7.2.1 Subcellular localization -- 7.2.2 Antigenic prediction -- 7.2.3 Sequence homology -- 7.3 Databases for reverse vaccinology -- 7.3.1 National Center for Biotechnology Information databases -- 7.3.2 UniProt-the universal proteins database -- 7.3.3 The Immune Epitope Database -- 7.4 In silico case study: reverse vaccinology on Mycoplasma genitalium -- 7.5 Conclusions -- References -- 8 Bacterial dynamics and network analysis for antigen screening -- 8.1 Background -- 8.2 Systematic biology and network analysis -- 8.2.1 Network analysis -- 8.2.1.1 Dendroscope 3 -- 8.2.1.2 SplitsTree4 -- 8.2.1.3 Reconstructing phylogenetic networks based on quartet and sextet -- 8.2.1.4 Search tool for retrieval of interacting genes/proteins -- 8.2.1.5 Identifying protein-protein interactions -- 8.3 Genome annotation -- 8.3.1 Rapid annotation using subsystem technology -- 8.3.2 Prokaryotic genome annotation pipeline -- 8.3.3 Rapid prokaryotic genome annotation -- 8.3.4 Annotation enrichment -- 8.4 Pangenomic tools -- 8.4.1 Pangenomes analysis pipeline -- 8.4.2 Rapid large-scale prokaryote pangenome analysis -- 8.4.3 Bacterial pangenome analysis tool -- 8.4.4 OrthoFinder -- 8.5 Gene transfer analysis tools -- 8.5.1 Genomic Island Prediction Software -- 8.5.2 IslandViewer -- 8.6 Conclusion -- References -- 9 Tools and platform for allergenicity prediction -- 9.1 Introduction -- 9.2 Databases and tools for allergens/allergenicity prediction -- 9.3 Case studies -- 9.4 Conclusion -- Ethics declarations -- Financial interests -- Conflicts of interest -- References -- 10 Screening of potential vaccine candidates through machine learning approach -- 10.1 Introduction to machine learning -- 10.2 Training of a machine learning model -- 10.2.1 Training dataset preparation -- 10.2.2 Dataset preprocessing -- 10.2.3 Feature selection.
  table of contents: 10.3 Machine learning approaches -- 10.3.1 Categories of machine learning approaches -- 10.3.1.1 Supervised learning -- 10.3.2 Regression and classification-tasks of supervised machine learning -- 10.3.3 Algorithms of supervised machine learning -- 10.3.3.1 Unsupervised learning -- 10.3.4 Algorithms of unsupervised machine learning -- 10.4 Performance measures -- 10.5 Applications of machine learning in reverse vaccinology -- 10.5.1 Genomic data analysis -- 10.5.2 Epitope prediction -- 10.5.3 Immunogenicity assessment -- 10.5.4 Adjuvant selection -- 10.5.5 Vaccine formulation optimization -- 10.6 Conclusion and prospects -- References -- 11 Reverse vaccinology 2.0: computational resources for B-cell epitope prediction -- 11.1 Introduction -- 11.2 Prediction of linear (continuous) B-cell epitopes -- 11.3 Prediction of conformational (discontinuous) B-cell epitopes -- 11.4 Case studies -- 11.5 Conclusion -- Abbreviations -- Funding -- Data availability statement -- Declaration of competing interest -- References -- 12 Structural vaccinology approaches to enhance efficacy, stability, and delivery of protective antigens -- 12.1 Introduction -- 12.2 Challenges in structure-based design of improved viral antigens -- 12.3 Phosphorylcholine -- 12.4 Malondialdehyde -- 12.5 Natural antibodies -- 12.6 Hepatitis C virus -- 12.7 Respiratory syncytial virus -- 12.8 Influenza virus -- 12.9 Dengue virus -- 12.10 Masking of nonneutralizing epitopes -- 12.11 Improvement of vaccine thermostability -- 12.12 Structural vaccinology pitfalls -- 12.13 Conclusion -- References -- 13 Next-gen sequencing-driven antigen screening technology in vaccine development -- 13.1 Introduction -- 13.2 Next-generation sequencing-a platform for screening the antigens -- 13.2.1 Next-generation sequencing platforms -- 13.2.1.1 Illumina -- 13.2.1.2 Semiconductor-based platforms.
  table of contents: 13.2.1.3 SOLiD platform -- 13.2.1.4 Complete Genomics analysis platform -- 13.2.1.5 Pyrosequencing -- 13.2.1.6 Targeted resequencing or enrichment strategies -- 13.2.1.7 Whole-exome sequencing -- 13.3 Artificial intelligence and machine learning-in silico-derived vaccines -- 13.4 Next-generation sequencing revolution in collective high-performance computing systems -- 13.4.1 Estimating viral genetic diversity using next-generation sequencing data-benefits and challenges -- 13.5 Reverse vaccinology approach against antibiotic-resistant bacteria -- 13.6 Conclusion -- References -- 3 Disease case study -- 14 Bioinformatics approach to design peptide vaccines for viruses -- 14.1 Introduction -- 14.2 Foot-and-mouth disease context -- 14.3 Foot-and-mouth disease vaccines over the years -- 14.3.1 Inactivated vaccines -- 14.3.2 Live-attenuated vaccines -- 14.3.3 DNA vaccines -- 14.3.4 Peptide vaccines -- 14.3.5 Vector vaccines -- 14.3.6 Virus-like particles -- 14.3.7 Vaccines overview -- 14.4 Reverse vaccinology applied to foot-and-mouth disease -- 14.5 Advances in computational vaccine development against foot-and-mouth disease virus -- 14.6 Adopting different strategies in reverse vaccinology against foot-and-mouth disease -- 14.7 Challenges and perspectives -- 14.8 Conclusion -- References -- 15 Confirmation of candidates identified by reverse vaccinology in animal models or other immunogenicity assays -- 15.1 Introduction -- 15.2 Stages of vaccine development -- 15.3 In vivo tests -- 15.3.1 Reverse vaccinology-developed vaccines tested in vivo -- 15.3.2 In vivo testing: limitations -- 15.4 In vitro tests -- 15.5 Conclusions and perspectives -- References -- 16 Clinical trials of vaccines incorporating antigens identified from a reverse vaccinology approach -- 16.1 Introduction -- 16.2 Reverse vaccinology -- 16.3 Clinical trials.
  note: edited by Jayashankar Das, Sushma Dave, Siomar de Castro Soares, Sandeep Tiwari.
  note: Includes bibliographical references and index.
  subject:
    topic: Vaccines
  subject:
    topic: Vaccins
  classification: QR189 .R48 2024
  classification: 615.3/72
  related item:
    identifier: (OCoLC)1389876653
  identifier: 9780443133961
  identifier: 0443133964
  identifier: 9780443133954
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 240711
    record change date: 20260327180120.1
    record identifier: in00005773380
    language of cataloging:
      language term: eng