Immunoinformatics: Predicting Immunogenicity in Silico
Darren R. Flower
Springer Science & Business Media, Jan 1, 2007 - Allergy and Immunology - 438 pages
Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting technology and provides examples in the major areas within the field of immunoinformatics. This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. The volume is conveniently divided into four sections. The first section, Databases, details various immunoinformatic databases, including IMGT/HLA, IPD, and SYEPEITHI. In the second section, Defining HLA Supertypes, authors discuss supertypes of GRID/CPCA and hierarchical clustering methods, Hla-Ad supertypes, MHC supertypes, and Class I Hla Alleles. The third section, Predicting Peptide-MCH Binding, includes discussions of MCH binders, T-Cell epitopes, Class I and II Mouse Major Histocompatibility, and HLA-peptide binding. Within the fourth section, Predicting Other Properties of Immune Systems, investigators outline TAP binding, B-cell epitopes, MHC similarities, and predicting virulence factors of immunological interest. Immunoinformatics: Predicting Immunogenicity In Silico merges skill sets of the lab-based and the computer-based science professional into one easy-to-use, insightful volume.
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algorithm alleles amino acid analysis antibodies antigen artificial neural network Bayesian Bcipep binding affinity binding groove binding peptides Bioinformatics cell computational CoMSIA cross-validation D. R. Flower data set descriptors display Doytchinova epitope prediction format function genes genome hapten HLA alleles human human leukocyte antigen IG and TR IMGT/HLA Database Immunogenetics Immunogenicity Immunogenicity In Silico Immunoinformatics Immunol immunology interaction Lefranc ligands major histocompatibility complex matrix menu MHC alleles MHC binders MHC class MHC class II MHC molecules MHC-binding MHCBN MHCI MHCII motifs neural networks nonbinders Nucleic Acids Res nucleotide option output parameters peptide binding peptide sequences peptide–MHC binding pMHC polymorphic positions Predicting Immunogenicity Prediction of MHC predictor proteasomal protein sequence provides PSSMs QSAR quantitative query receptor residues score server Silico Silico Edited simulation specific supertypes support vector machine T-cell epitopes Table threshold vaccine variables