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
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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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.
Immunogenicity:Predicting Immunogenicity in silico[NOTE: As these papers describe computational methods, NONE are in thestrict MiMB format, though most approximate it. This I have discussedwith John Walker, and he indicates that this is acceptable. I indicatebelow those papers which do not even have a MiMB-like format.]0. Preface[THIS IS NOT IN MiMB FORMAT]1. Immunoinformatics and the in silico prediction of Immunogenicity:An introduction.Darren R Flower[THIS IS NOT IN MiMB FORMAT]Section 1: Databases2. IMGT®, the international ImMunoGeneTics information system® forimmunoinformatics. Methods for querying IMGT® databases, tools and Webresources in the context of immunoinformaticsMarie-Paule Lefranc[Prof LeFranc has agreed to pay for colour figures, but needs to bebilled.]3. The IMGT/HLA DatabaseJames Robinson and Steven G. E. Marsh4. IPD - the Immuno Polymorphism DatabaseJames Robinson and Steven G. E. Marsh5. SYFPEITHI: Database for Searching and T-Cell Epitope PredictionMathias M. Schuler, Maria-Dorothea Nastke and Stefan Stevanovi_6. Searching and Mapping of T cell epitopes, MHC binders, and TAPbindersManoj Bhasin, Sneh Lata and Gajendra P S Raghava7. Searching and Mapping of B-cell epitopes in Bcipep databaseSudipto Saha and Gajendra P.S. Raghava8. Searching haptens, carrier proteins and anti-hapten antibodiesShilpy Srivastava, Mahender Kumar Singh, Gajendra P S Raghavaand G. C. VarshneySection 2: Defining HLA Supertypes9. The classification of HLA supertypes by GRID/CPCAand hierarchical clustering methodsPingping Guan, Irini A. Doytchinova and Darren R. Flower10. Structural Basis For Hla-A2 SupertypesPandjassarame Kangueane and Meena Kishore Sakharkar11. Definition of MHC Supertypes Through Clustering ofMHC Peptide-bindingRepertoiresPedro A. Reche and Ellis L. Reinherz12. Grouping Of Class I Hla Alleles Using Electrostatic DistributionMapsOf The Peptide Binding Grooves.Pandjassarame Kangueane and Meena Kishore SakharkarSection 3: Predicting peptide-MHC binding13. Predicton of Peptide-MHC Binding Using ProfilesPedro A. Reche and Ellis L. Reinherz14. Application of machine learning techniques in predicting MHC bindersSneh Lata, Manoj Bhasin and G P S Raghava15. Artificial Intelligence Methods for Predicting T-Cell EpitopesYingdong Zhao, Myong-Hee Sung, Richard Simon16. Towards the Prediction of Class I and II Mouse MajorHistocompatibilityComplex Peptide Binding Affinity: In Silico Bioinformatic Step by StepGuide Using Quantitative Structure-Activity RelationshipsChanna K. Hattotuwagama, Irini A. Doytchinova, & Darren R. Flower17. Predicting the MHC-peptide affinity using some interactive typemolecular deillegalscriptors and QSAR modelsThy-Hou Lin18. Implementing the Modular MHC Model for Predicting Peptide BindingDavid S. DeLuca and Rainer Blasczyk19. Support vector machine-based prediction of MHC binding peptidesPierre Dönnes 20. In silico prediction of peptide MHC binding affinity using SVRMHCWen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower and Tongbin Li21. HLA-Peptide Binding Prediction Using Structural And ModelingPrinciplesPandjassarame Kangueane and Meena Kishore Sakharkar22. A Practical Guide to Structure-based Prediction of MHC BindingPeptidesShoba Ranganathan and Joo Chuan Tong23. Static Energy Analysis of MHC Class I and Class II-peptide bindingaffinityMatthew N. Davies and Darren R. Flower24. Molecular dynamics simulations:bring biomolecular structures alive on a computerShunzhou Wan, Peter V. Coveney, & Darren R. Flower25. An Iterative Approach to Class II
"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
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 Deillegalscriptors 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.
"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
Inhaltsverzeichnis
Immunogenicity:
Predicting Immunogenicity in silico
[NOTE: As these papers describe computational methods, NONE are in the
strict MiMB format, though most approximate it. This I have discussed
with John Walker, and he indicates that this is acceptable. I indicate
below those papers which do not even have a MiMB-like format.]
0. Preface
[THIS IS NOT IN MiMB FORMAT]
1. Immunoinformatics and the in silico prediction of Immunogenicity:
An introduction.
Darren R Flower
[THIS IS NOT IN MiMB FORMAT]
Section 1: Databases
2. IMGT®, the international ImMunoGeneTics information system® for
immunoinformatics. Methods for querying IMGT® databases, tools and Web
resources in the context of immunoinformatics
Marie-Paule Lefranc
[Prof LeFranc has agreed to pay for colour figures, but needs to be
billed.]
3. The IMGT/HLA Database
James Robinson and Steven G. E. Marsh
4. IPD - the Immuno Polymorphism Database
James Robinson and Steven G. E. Marsh
5. SYFPEITHI: Database for Searching and T-Cell Epitope Prediction
Mathias M. Schuler, Maria-Dorothea Nastke and Stefan Stevanovi_
6. Searching and Mapping of T cell epitopes, MHC binders, and TAP
binders
Manoj Bhasin, Sneh Lata and Gajendra P S Raghava
7. Searching and Mapping of B-cell epitopes in Bcipep database
Sudipto Saha and Gajendra P.S. Raghava
8. Searching haptens, carrier proteins and anti-hapten antibodies
Shilpy Srivastava, Mahender Kumar Singh, Gajendra P S Raghava
and G. C. Varshney
Section 2: Defining HLA Supertypes
9. The classification of HLA supertypes by GRID/CPCA
and hierarchical clustering methods
Pingping Guan, Irini A. Doytchinova and Darren R. Flower
10. Structural Basis For Hla-A2 Supertypes
Pandjassarame Kangueane and Meena Kishore Sakharkar
11. Definition of MHC Supertypes Through Clustering of
MHC Peptide-bindingRepertoires
Pedro A. Reche and Ellis L. Reinherz
12. Grouping Of Class I Hla Alleles Using Electrostatic Distribution
Maps
Of The Peptide Binding Grooves.
Pandjassarame Kangueane and Meena Kishore Sakharkar
Section 3: Predicting peptide-MHC binding
13. Predicton of Peptide-MHC Binding Using Profiles
Pedro A. Reche and Ellis L. Reinherz
14. Application of machine learning techniques in predicting MHC binders
Sneh Lata, Manoj Bhasin and G P S Raghava
15. Artificial Intelligence Methods for Predicting T-Cell Epitopes
Yingdong Zhao, Myong-Hee Sung, Richard Simon
16. Towards 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
Channa K. Hattotuwagama, Irini A. Doytchinova, & Darren R. Flower
17. Predicting the MHC-peptide affinity using some interactive type
molecular deillegalscriptors and QSAR models
Thy-Hou Lin
18. Implementing the Modular MHC Model for Predicting Peptide Binding
David S. DeLuca and Rainer Blasczyk
19. Support vector machine-based prediction of MHC binding peptides
Pierre Dönnes
20. In silico prediction of peptide MHC binding affinity using SVRMHC
Wen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower and Tongbin Li
21. HLA-Peptide Binding Prediction Using Structural And Modeling
Principles
Pandjassarame Kangueane and Meena Kishore Sakharkar
22. A Practical Guide to Structure-based Prediction of MHC Binding
Peptides
Shoba Ranganathan and Joo Chuan Tong
23. Static Energy Analysis of MHC Class I and Class II-peptide binding
affinity
Matthew N. Davies and Darren R. Flower
24. Molecular dynamics simulations:
bring biomolecular structures alive on a computer
Shunzhou Wan, Peter V. Coveney, & Darren R. Flower
25. An Iterative Approach to Class II
Klappentext
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.
Addresses databases, HLA supertypes, MCH binding, and other properties of immune systemsnA firm background for anyone working in immunoinformaticsnChapters written by leaders in the field
