Chapter 7 Immunoinformatics: computational keys to immune system secrets
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Vikas Kushwaha
Abstract
Immuno-informatics is an interdisciplinary field that leverages computational approaches to study and understand immune system responses of the host against the pathogens. It a new technology that deals with combinations of immunology with bioinformatics, molecular modelling, and systems biology to analyze large datasets, predict immune responses, and develop new strategies for vaccine design, diagnostics, and immunotherapies. Now, availability of genomic, proteomic, and immunological data, immuno-informatics has become an essential tool in modern immunology research, accelerating the development of new vaccines and therapeutics. The main objectives of immuno-informatics are the prediction of epitopes, which are specific regions on antigens that can be recognized by the immune system, particularly by T-cells and B-cells. Epitope prediction allows for the identification of potential targets for vaccine development, avoiding the need for expensive and time-consuming experimental approaches. Computational tools like machine learning models and molecular docking simulations are widely employed to identify and predict these immunogenic regions from pathogens, cancer cells, or allergens. Additionally, immuno-informatics help in understanding the complex interactions between pathogens and the host immune system. It helps in mapping out the genetic variations in pathogens, immune evasion strategies, and host immune responses, which can be further used to develop personalized immunotherapies and improve the efficacy of vaccines. Overall, immunoinformatics holds significant promise in advancing our understanding of immune responses, driving innovation in disease prevention and treatment through computational tools that enhance the accuracy and efficiency of immunological research.
Abstract
Immuno-informatics is an interdisciplinary field that leverages computational approaches to study and understand immune system responses of the host against the pathogens. It a new technology that deals with combinations of immunology with bioinformatics, molecular modelling, and systems biology to analyze large datasets, predict immune responses, and develop new strategies for vaccine design, diagnostics, and immunotherapies. Now, availability of genomic, proteomic, and immunological data, immuno-informatics has become an essential tool in modern immunology research, accelerating the development of new vaccines and therapeutics. The main objectives of immuno-informatics are the prediction of epitopes, which are specific regions on antigens that can be recognized by the immune system, particularly by T-cells and B-cells. Epitope prediction allows for the identification of potential targets for vaccine development, avoiding the need for expensive and time-consuming experimental approaches. Computational tools like machine learning models and molecular docking simulations are widely employed to identify and predict these immunogenic regions from pathogens, cancer cells, or allergens. Additionally, immuno-informatics help in understanding the complex interactions between pathogens and the host immune system. It helps in mapping out the genetic variations in pathogens, immune evasion strategies, and host immune responses, which can be further used to develop personalized immunotherapies and improve the efficacy of vaccines. Overall, immunoinformatics holds significant promise in advancing our understanding of immune responses, driving innovation in disease prevention and treatment through computational tools that enhance the accuracy and efficiency of immunological research.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Contributors VII
- Chapter 1 Introduction to bioinformatics 1
- Chapter 2 Biological databases and bioinformatics tools 13
- Chapter 3 Fundamentals of bioinformatics 41
- Chapter 4 Tools used in sequence alignment 61
- Chapter 5 Recent advances in the discovery of drug molecules: trends, scope, and relevance 83
- Chapter 6 Computer-aided drug design and drug discovery 103
- Chapter 7 Immunoinformatics: computational keys to immune system secrets 123
- Chapter 8 Phylogenetic analysis 141
- Chapter 9 Basic structure of proteins: current paradigms, trends, and perspective 151
- Index 171
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Contributors VII
- Chapter 1 Introduction to bioinformatics 1
- Chapter 2 Biological databases and bioinformatics tools 13
- Chapter 3 Fundamentals of bioinformatics 41
- Chapter 4 Tools used in sequence alignment 61
- Chapter 5 Recent advances in the discovery of drug molecules: trends, scope, and relevance 83
- Chapter 6 Computer-aided drug design and drug discovery 103
- Chapter 7 Immunoinformatics: computational keys to immune system secrets 123
- Chapter 8 Phylogenetic analysis 141
- Chapter 9 Basic structure of proteins: current paradigms, trends, and perspective 151
- Index 171
