Immunoinformatics : predicting immunogenicity in silico / edited by Darren R. Flower.

Contributor(s): Flower, Darren R
Material type: TextTextSeries: Methods in molecular biology (Clifton, N.J.): v. 409.Publisher: Totowa, N.J. : Humana, ©2007Description: 1 online resource (xv, 438 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9781603271189; 160327118X; 1280945257; 9781280945250; 661094525X; 9786610945252Subject(s): Immunoinformatics | Immunology -- Computer simulation | Immunological tolerance -- Computer simulation | Computational Biology -- methods | Immune System | Models, Immunological | Models, Theoretical | Allergy and Immunology | Medical Informatics | Immunogenetics | Databases, Factual | Methods | Computational Biology | Biology | Databases as Topic | Investigative Techniques | Genetics | Information Science | Medicine | Hemic and Immune Systems | Models, Biological | Informatics | Analytical, Diagnostic and Therapeutic Techniques and Equipment | Biological Science Disciplines | Anatomy | Information Storage and Retrieval | Health Occupations | Natural Science Disciplines | Disciplines and Occupations | SCIENCE -- Life Sciences -- Anatomy & Physiology | Immunological tolerance -- Computer simulation | Allergy and Immunology | Computational Biology -- methods | Medical Informatics -- methods | Immunogenetics -- methods | Databases, Factual | Immunoinformatics | Immunology -- Computer simulation | Immunoinformatics | Immunology -- Computer simulation | immunologie | immunology | informatica | informatics | Genome informatics | GenoominformaticaGenre/Form: Electronic books. Additional physical formats: Print version:: Immunoinformatics.DDC classification: 571.960285 LOC classification: QR182.2.I46 | I463 2007Other classification: R392 Online resources: Click here to access online
Contents:
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.
Summary: 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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Includes bibliographical references and index.

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.

Print version record.

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.

English.

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