Immunoinformatics (eBook, PDF)

Predicting Immunogenicity In Silico
Redaktion: Flower, Darren R.

• Format: PDF

Produktbeschreibung

This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. It addresses databases, HLA supertypes, MCH binding, and other properties of immune systems. The book contains chapters written by leaders in the field and provides a firm background for anyone working in immunoinformatics in one easy-to-use, insightful volume.

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Produktdetails

• Verlag: Humana Press
• Seitenzahl: 438
• Erscheinungstermin: 7. Februar 2008
• Englisch
• ISBN-13: 9781603271189
• Artikelnr.: 64701120

Autorenporträt

Darren R. Flower, Edward Jenner Institute for Vaccine Research, Compton, UK

Inhaltsangabe

Databases.- IMGT®, the International ImmunoGeneTics Information System® for Immunoinformatics.- The IMGT/HLA Database.- IPD.- SYFPEITHI.- Searching and Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders.- Searching and Mapping of B-Cell Epitopes in Bcipep Database.- Searching Haptens, Carrier Proteins, and Anti-Hapten Antibodies.- Defining HLA Supertypes.- The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods.- Structural Basis for HLA-A2 Supertypes.- Definition of MHC Supertypes Through Clustering of MHC Peptide-Binding Repertoires.- Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves.- Predicting Peptide-MHC Binding.- Prediction of Peptide-MHC Binding Using Profiles.- Application of Machine Learning Techniques in Predicting MHC Binders.- Artificial Intelligence Methods for Predicting T-Cell Epitopes.- Toward the Prediction of Class I and II Mouse Major Histocompatibility Complex-Peptide-Binding Affinity.- Predicting the MHC-Peptide Affinity Using Some Interactive-Type Molecular Descriptors and QSAR Models.- Implementing the Modular MHC Model for Predicting Peptide Binding.- Support Vector Machine-Based Prediction of MHC-Binding Peptides.- In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC.- HLA-Peptide Binding Prediction Using Structural and Modeling Principles.- A Practical Guide to Structure-Based Prediction of MHC-Binding Peptides.- Static Energy Analysis of MHC Class I and Class II Peptide-Binding Affinity.- Molecular Dynamics Simulations.- An Iterative Approach to Class II Predictions.- Building a Meta-Predictor for MHC Class II-Binding Peptides.- Nonlinear Predictive Modeling of MHC Class II-Peptide Binding Using Bayesian Neural Networks.- Predicting otherProperties of Immune Systems.- TAPPred Prediction of TAP-Binding Peptides in Antigens.- Prediction Methods for B-cell Epitopes.- HistoCheck.- Predicting Virulence Factors of Immunological Interest.- Immunoinformatics and the in Silico Prediction of Immunogenicity.- Immunoinformatics and the in Silico Prediction of Immunogenicity.

Rezensionen

"Investigators considering problems of recombinant vaccine design, possible host responses, and how to select likely sites from a large pool of information (the protein of interest) will find valuable material here." -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars

"...a value to virtually any investigator in this general field." -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars

"...a valuable addition to libraries in universities and research institutes, R & D firms engaged in the development of vaccines and immunotherapeutics, and clinical research centres." -Immunology news