Immunoinformatics and the in silico prediction of immunogenicity. An introduction / D.R. Flower -- Imgt, the international immunogenetics information system for immunoinformatics. Methods for querying imgt databases, tools, and web resources in the context of immunoinformatics / M.P. Lefranc -- The imgt/hla database / J. Robinson and S.G. Marsh -- Ipd: The immuno polymorphism database / J. Robinson and S.G. Marsh -- Syfpeithi: Database for searching and t-cell epitope prediction / M.M. Schuler, M.D. Nastke and S. Stevanovikc -- Searching and mapping of t-cell epitopes, mhc binders, and tap binders / M. Bhasin, S. Lata and G.P. Raghava -- Searching and mapping of b-cell epitopes in bcipep database / S. Saha and G.P. Raghava -- Searching haptens, carrier proteins, and anti-hapten antibodies / S. Srivastava [and others] -- The classification of hla supertypes by grid/cpca and hierarchical clustering methods / P. Guan, I.A. Doytchinova and D.R. Flower -- Structural basis for hla-a2 supertypes / P. Kangueane and M.K. Sakharkar -- Definition of mhc supertypes through clustering of mhc peptide-binding repertoires / P.A. Reche and E.L. Reinherz -- Grouping of class i hla alleles using electrostatic distribution maps of the peptide binding grooves / P. Kangueane and M.K. Sakharkar -- Prediction of peptide-mhc binding using profiles / P.A. Reche and E.L. Reinherz -- Application of machine learning techniques in predicting mhc binders / S. Lata, M. Bhasin and G.P. Raghava -- Artificial intelligence methods for predicting t-cell epitopes / Y. Zhao, M.H. Sung and R. Simon -- Toward the prediction of class i and ii mouse major histocompatibility complex-peptide-binding affinity: In silico bioinformatic step-by-step guide using quantitative structure-activity relationships / C.K. Hattotuwagama, I.A. Doytchinova and D.R. Flower -- Predicting the mhc-peptide affinity using some interactive-type molecular descriptors and qsar models / T.H. Lin -- Implementing the modular mhc model for predicting peptide binding / D.S. DeLuca and R. Blasczyk -- Support vector machine-based prediction of mhc-binding peptides / P. Donnes -- In silico prediction of peptide-mhc binding affinity using svrmhc / W. Liu [and others] -- Hla-peptide binding prediction using structural and modeling principles / P. Kangueane and M.K. Sakharkar -- A practical guide to structure-based prediction of mhc-binding peptides / S. Ranganathan and J.C. Tong -- Static energy analysis of mhc class i and class ii peptide-binding affinity / M.N. Davies and D.R. Flower -- Molecular dynamics simulations: Bring biomolecular structures alive on a computer / S. Wan, P.V. Coveney and D.R. Flower -- An iterative approach to class ii predictions / R.R. Mallios -- Building a meta-predictor for mhc class ii-binding peptides / L. Huang [and others] -- Nonlinear predictive modeling of mhc class ii-peptide binding using bayesian neural networks / D.A. Winkler and F.R. Burden -- Tappred prediction of tap-binding peptides in antigens / M. Bhasin, S. Lata and G.P. Raghava -- Prediction methods for b-cell epitopes / S. Saha and G.P. Raghava -- Histocheck. Evaluating structural and functional mhc similarities / D.S. DeLuca and R. Blasczyk -- Predicting virulence factors of immunological interest / S. Saha and G.P. Raghava -- Immunoinformatics. Predicting immunogenicity in silico. Preface / D.R. Flower.

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.