Namrata Tomar - Immunoinformatics

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The immune system is very complex and consists of numerous cell types, molecular pathways, and signals, which help a host system to distinguish between normal, healthy cells and unhealthy cells. All immune cell types have a specific role and ways of recognizing potentially harmful foreign bodies. To get the diverse details of an immune network, a researcher may optimize immune responses for a specific issue that may range from minor infections to cancer. This requires implementing data mining, statistics, and machine learning approaches to convert high-throughput immune data into meaningful insights. In simpler terms,Immunoinformaticsincorporates the application of bioinformatics methods, mathematical models, and statistical techniques for the study of immune systems biology. The development of immunoinformatics tools, databases, and models involves computer scientists and modeling experts working closely with immunologists in a multidisciplinary team. Modeling and computational approaches have been widely applied to solve the problems in immunology as in quantifying the data generated in laboratory experiments and extracting meaningful biological information on its kinetics. To state the value of computational tools and models in immunology research, we need a variety of immune system-related databases, prediction software and modeling tools, informatics, and computational infrastructure for connecting computer modeling and wet-lab experimentation, as well as data analytics and visualization.

This book consists of 23 chapters that cover diverse immunoinformatics research topics. It involves tools and databases of potential epitope prediction, HLA gene analysis, MHC characterizing, in silico vaccine design, mathematical modeling of host-pathogen interactions, and network analysis of immune system data.

Chapterintroduces a reverse vaccinology approach and its advantages and applications. It basically searches through genomic sequences to predict antigens that have a capacity to be used as potential vaccine candidates. It describes required web tools, databases, and software to predict potential epitopes for vaccine development.

Chapterintroduces a peptide-based vaccine approach to design an in silico vaccine against Zika virus.

Chapterfocuses on high-definition genomic analysis of human leukocyte antigen (HLA) genes that encode for major histocompatibility complex (MHC) proteins. The genotyping of HLA alleles was done through whole genome sequencing data, whole exome sequencing data, or targeting sequence of HLA genes by using next-generation sequencing technology.

Chapterdescribes detailed steps for a computational vaccine design for MERS-CoV infections. It mostly makes use of IEDB software to predict the suitable MERS-CoV epitope vaccine.

Chapterintroduces an alignment-independent platform for allergenicity prediction. It utilizes three modular servers to assess the allergenicity of a randomly selected allergenic protein and demonstrates a protocol for fast and reliable in silico prediction.

Chapterreviews the methodology used for computational identification of B and T cell epitopes against enterotoxigenicEscherichia coli,along with other databases of epitopes and analysis tools for T cell and B cell epitope prediction and vaccine design.

Chapterintroduces immunoinformatics and molecular docking methods to screen potential vaccine candidates for Leptospira, which is responsible for Leptospirosis, a zoonotic disease.

Chaptertakes into account a residue-centric presentation score for both mutated residues and MHC-I genotype and hypothesized that high scores (corresponds to poor presentation) would correlate to high mutation frequencies within tumors. To explain, MHC class I proteins present on the cells surface recognize tumor-specific neoantigens of early neoplastic cells and eliminate them before the tumor develops.

Chaptersuggests the importance of the network analysis of large-scale data and its application in the field of immunological research. Network analysis is a way to extract complex information from high-throughput data and develop advanced algorithms to unveil the underlying mechanisms. This chapter discusses the ways to integrate and analyze networks using genome-wide transcriptional profiles.