Reverse vaccinology : concept, methods and advancement /

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...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Das, Jayashankar (Editor), Dave, Sushma (Editor), Soares, Siomar (Editor), Tiwari, Sandeep (Editor)
Format:	eBook
Language:	English
Published:	London, United Kingdom ; San Diego, CA : Academic Press, [2024]
Subjects:	Vaccines. Vaccins. Electronic books.
Online Access:	Connect to the full text of this electronic book

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.
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.
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.
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.
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.