Online citations, reference lists, and bibliographies.

Structure-based Prediction Of Major Histocompatibility Complex (MHC) Epitopes.

Andrew J. Bordner
Published 2013 · Medicine, Biology

Cite This
Download PDF
Analyze on Scholarcy
Share
Because of the enormous diversity of both MHC proteins and peptide epitopes, computational epitope prediction methods are needed in order to supplement limited experimental data. These prediction methods are useful for guiding experiments and have many potential biomedical applications. Unlike popular sequence-based methods, structure-based epitope prediction methods can predict epitopes for multiple MHC types with highly distinct peptide binding propensities. In this chapter, we describe in detail our previously developed structure-based epitope prediction methods for both class I and class II MHC proteins. We also discuss the relative advantages and disadvantages of sequence-based versus structure-based methods and how to evaluate prediction performance.
This paper references
10.1110/PS.9.9.1838
Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles.
O. Schueler-Furman (2000)
10.1186/1745-7580-1-4
AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data
C. Toseland (2005)
10.1073/pnas.86.24.10049
HLA-DR4 subtype frequencies in rheumatoid arthritis indicate that DRB1 is the major susceptibility locus within the HLA class II region.
B. P. Wordsworth (1989)
10.1084/JEM.189.12.1885
Immunoglobulin E–independent Major Histocompatibility Complex–restricted T Cell Peptide Epitope–induced Late Asthmatic Reactions
B. Haselden (1999)
10.1371/JOURNAL.PBIO.0030091
The immune epitope database and analysis resource: from vision to blueprint.
A. Sette (2004)
10.1002/pro.732
HLA-DP2 binding prediction by molecular dynamics simulations.
I. Doytchinova (2011)
10.1371/journal.pone.0009272
Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules
H. Zhang (2010)
10.1016/S0091-6749(98)70358-6
Effects of peptide therapy on ex vivo T-cell responses.
G. V. Marcotte (1998)
10.1186/1471-2105-11-568
Peptide binding predictions for HLA DR, DP and DQ molecules
P. Wang (2010)
10.1186/1471-2105-10-296
NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
M. Nielsen (2009)
10.1016/S0091-6749(98)70402-6
Successful immunotherapy with T-cell epitope peptides of bee venom phospholipase A2 induces specific T-cell anergy in patients allergic to bee venom.
U. Müller (1998)
10.1002/1521-4141(200006)30:6<1638::AID-IMMU1638>3.0.CO;2-R
Allergen-derived long peptide immunotherapy down-regulates specific IgE response and protects from anaphylaxis.
C. von Garnier (2000)
10.1016/0140-6736(92)90571-J
DQ (rather than DR) gene marks susceptibility to narcolepsy
K. Matsuki (1992)
10.1086/318799
Complex HLA-DR and -DQ interactions confer risk of narcolepsy-cataplexy in three ethnic groups.
E. Mignot (2001)
10.2119/2003-00032.SANSOM
A Novel Predictive Technique for the MHC Class II Peptide-Binding Interaction
M. Davies (2003)
10.1016/J.COI.2006.09.013
Immunoregulation by targeting T cells in the treatment of allergy and asthma.
M. Larché (2006)
10.1016/J.TIPS.2004.10.011
The advantages and limitations of protein crystal structures.
K. Acharya (2005)
10.1111/J.1399-0039.1995.TB02503.X
The multiple sclerosis- and narcolepsy-associated HLA class II haplotype includes the DRB5*0101 allele.
A. Fogdell (1995)
10.1186/1756-0500-2-61
MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes
S. Lata (2008)
10.1186/1471-2105-11-41
Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model
A. Bordner (2009)
10.1023/A:1010933404324
Random Forests
L. Breiman (2004)
10.1056/NEJM199006283222602
Analysis of HLA-DQ genotypes and susceptibility in insulin-dependent diabetes mellitus.
J. Baisch (1990)
10.1093/bioinformatics/btl216
DynaPred: A structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations
I. Antes (2006)
10.1093/nar/gkq1021
The RCSB Protein Data Bank: redesigned web site and web services
P. Rose (2011)
10.1371/journal.pone.0003268
On Evaluating MHC-II Binding Peptide Prediction Methods
Yasser El-Manzalawy (2008)
10.1084/jem.169.1.345
Evidence for a primary association of celiac disease to a particular HLA-DQ alpha/beta heterodimer
L. Sollid (1989)
10.1093/bioinformatics/btl071
Prediction of HLA-DQ3.2ß Ligands: evidence of multiple registers in class II binding peptides
J. C. Tong (2006)
10.1186/1471-2105-9-S12-S22
Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research
H. Lin (2008)
10.1016/j.drudis.2009.01.003
Managing protein flexibility in docking and its applications.
Chandrika B-Rao (2009)
10.1371/journal.pone.0014383
Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes
A. Bordner (2010)
10.1006/jmbi.1994.1052
Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins.
R. Abagyan (1994)
10.1110/PS.19202
Soft protein-protein docking in internal coordinates.
J. Fernández-Recio (2002)
10.1016/j.jim.2011.07.007
Dana-Farber repository for machine learning in immunology.
G. L. Zhang (2011)
10.1073/PNAS.93.2.734
Crystallographic analysis of endogenous peptides associated with HLA-DR1 suggests a common, polyproline II-like conformation for bound peptides.
T. Jardetzky (1996)
10.1093/nar/gks438
Immune epitope database analysis resource
Yohan Kim (2012)
10.1186/1472-6807-11-32
Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach
Atanas Patronov (2011)
10.1073/pnas.0936151100
Autoreactive T cells can be protected from tolerance induction through competition by flanking determinants for access to class II MHC
E. Maverakis (2003)
10.1016/S1074-7613(01)00202-3
Genetic protection from the inflammatory disease type 1 diabetes in humans and animal models.
J. Todd (2001)
10.1086/380997
Mapping multiple sclerosis susceptibility to the HLA-DR locus in African Americans.
J. Oksenberg (2004)
10.1021/JA032018Q
Structural mining: self-consistent design on flexible protein-peptide docking and transferable binding affinity potential.
Z. Liu (2004)
10.1007/s10822-009-9259-2
A critical cross-validation of high throughput structural binding prediction methods for pMHC
B. Knapp (2009)
10.1073/pnas.1018165108
Large-scale characterization of peptide-MHC binding landscapes with structural simulations
C. Yanover (2011)
10.1002/PROT.20831
Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes.
A. Bordner (2006)
10.1002/PROT.10608
Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization.
H. D. Schafroth (2004)
10.1016/S0140-6736(02)09332-7
Effect of T-cell peptides derived from Fel d 1 on allergic reactions and cytokine production in patients sensitive to cats: a randomised controlled trial
W. Oldfield (2002)
10.1110/PS.8.2.361
Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes.
M. Betancourt (1999)
10.1038/329599a0
HLA-DQβ gene contributes to susceptibility and resistance to insulin-dependent diabetes mellitus
John A. Todd (1987)
10.1146/annurev.iy.13.040195.003103
The three-dimensional structure of peptide-MHC complexes.
D. Madden (1995)
10.1021/JM9910775
Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins.
D. Rognan (1999)
10.1110/PS.03348304
An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state.
C. Zhang (2004)



This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar