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  • © 2007

Immunoinformatics

Predicting Immunogenicity In Silico

  • Addresses databases, HLA supertypes, MCH binding, and other properties of immune systems

  • A firm background for anyone working in immunoinformatics

  • Chapters written by leaders in the field

Part of the book series: Methods in Molecular Biology (MIMB, volume 409)

Buying options

eBook USD 139.00
Price excludes VAT (USA)
  • ISBN: 978-1-60327-118-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 179.99
Price excludes VAT (USA)
Hardcover Book USD 219.99
Price excludes VAT (USA)

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Table of contents (31 protocols)

  1. Front Matter

    Pages i-xv
  2. Immunoinformatics and the in Silico Prediction of Immunogenicity

    1. Front Matter

      Pages 1-1
  3. Databases

    1. Front Matter

      Pages 19-19
    2. The IMGT/HLA Database

      • James Robinson, Steven G.E. Marsh
      Pages 43-60
    3. IPD

      • James Robinson, Steven G. E. Marsh
      Pages 61-74
    4. SYFPEITHI

      • Mathias M. Schuler, Maria-Dorothea Nastke, Stefan Stevanović
      Pages 75-93
    5. Searching and Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders

      • Manoj Bhasin, Sneh Lata, Gajendra P. S. Raghava
      Pages 95-112
    6. Searching and Mapping of B-Cell Epitopes in Bcipep Database

      • Sudipto Saha, Gajendra P.S. Raghava
      Pages 113-124
    7. Searching Haptens, Carrier Proteins, and Anti-Hapten Antibodies

      • Shilpy Srivastava, Mahender Kumar Singh, Gajendra P.S. Raghava, Grish C. Varshney
      Pages 125-139
  4. Defining HLA Supertypes

    1. Front Matter

      Pages 143-143
    2. The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods

      • Pingping Guan, Irini A. Doytchinova, Darren R. Flower
      Pages 143-154
    3. Structural Basis for HLA-A2 Supertypes

      • Pandjassarame Kangueane, Meena Kishore Sakharkar
      Pages 155-162
  5. Predicting Peptide-MHC Binding

    1. Front Matter

      Pages 185-185
    2. Prediction of Peptide-MHC Binding Using Profiles

      • Pedro A. Reche, Ellis L. Reinherz
      Pages 185-200
    3. Application of Machine Learning Techniques in Predicting MHC Binders

      • Sneh Lata, Manoj Bhasin, Gajendra P.S. Raghava
      Pages 201-215
    4. Artificial Intelligence Methods for Predicting T-Cell Epitopes

      • Yingdong Zhao, Myong-Hee Sung, Richard Simon
      Pages 217-225

About this book

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.

Keywords

  • Allele
  • Antigen
  • Computer
  • In silico
  • artificial intelligence
  • calculus
  • database
  • databases
  • genetics
  • machine learning
  • modeling

Reviews

"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

Editors and Affiliations

  • The Jenner Institute, University of Oxford, Berkshire, UK

    Darren R. Flower

Bibliographic Information

  • Book Title: Immunoinformatics

  • Book Subtitle: Predicting Immunogenicity In Silico

  • Editors: Darren R. Flower

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-60327-118-9

  • Publisher: Humana Totowa, NJ

  • eBook Packages: Springer Protocols

  • Copyright Information: Humana Press 2007

  • Hardcover ISBN: 978-1-58829-699-3

  • Softcover ISBN: 978-1-61737-725-9

  • eBook ISBN: 978-1-60327-118-9

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XV, 438

  • Number of Illustrations: 106 b/w illustrations, 5 illustrations in colour

  • Topics: Bioinformatics, Life Sciences, Theory of Computation, Immunology, Medical Genetics, Cell Biology

Buying options

eBook USD 139.00
Price excludes VAT (USA)
  • ISBN: 978-1-60327-118-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 179.99
Price excludes VAT (USA)
Hardcover Book USD 219.99
Price excludes VAT (USA)