Predicting Immunogenicity In Silico

  • Darren R. Flower

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

Table of contents

  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. James Robinson, Steven G.E. Marsh
      Pages 43-60
    3. IPD
      James Robinson, Steven G. E. Marsh
      Pages 61-74
    4. Mathias M. Schuler, Maria-Dorothea Nastke, Stefan Stevanović
      Pages 75-93
    5. Manoj Bhasin, Sneh Lata, Gajendra P. S. Raghava
      Pages 95-112
    6. Sudipto Saha, Gajendra P.S. Raghava
      Pages 113-124
    7. 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. Pingping Guan, Irini A. Doytchinova, Darren R. Flower
      Pages 143-154
    3. Pandjassarame Kangueane, Meena Kishore Sakharkar
      Pages 155-162
  5. Predicting Peptide-MHC Binding

    1. Front Matter
      Pages 185-185
    2. Pedro A. Reche, Ellis L. Reinherz
      Pages 185-200
    3. Sneh Lata, Manoj Bhasin, Gajendra P.S. Raghava
      Pages 201-215
    4. Yingdong Zhao, Myong-Hee Sung, Richard Simon
      Pages 217-225
    5. Channa K. Hattotuwagama, Irini A. Doytchinova, Darren R. Flower
      Pages 227-245
    6. David S. DeLuca, Rainer Blasczyk
      Pages 261-271
    7. Wen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower, Tongbin Li
      Pages 283-291
    8. Pandjassarame Kangueane, Meena Kishore Sakharkar
      Pages 293-299
    9. Shoba Ranganathan, Joo Chuan Tong
      Pages 301-308
    10. Matthew N. Davies, Darren R. Flower
      Pages 309-320
    11. Shunzhou Wan, Peter V. Coveney, Darren R. Flower
      Pages 321-339
    12. Ronna Reuben Mallios
      Pages 341-353
    13. Lei Huang, Oleksiy Karpenko, Naveen Murugan, Yang Dai
      Pages 355-364
  6. Predicting other Properties of Immune Systems

    1. Front Matter
      Pages 381-381
    2. Manoj Bhasin, Sneh Lata, G.P.S. Raghava
      Pages 381-386
    3. Sudipto Saha, Gajendra P.S. Raghava
      Pages 387-394
    4. David S. DeLuca, Rainer Blasczyk
      Pages 395-405
    5. Sudipto Saha, Gajendra P.S. Raghava
      Pages 407-415
  7. Back Matter
    Pages 417-438

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.


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

Editors and affiliations

  • Darren R. Flower
    • 1
  1. 1.The Jenner InstituteUniversity of OxfordBerkshireUK

Bibliographic information

  • DOI
  • Copyright Information Humana Press 2007
  • Publisher Name Humana Press
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-58829-699-3
  • Online ISBN 978-1-60327-118-9
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site
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