Major Histocompatibility Complex. Principles of Immune Response Highly specific recognition of...
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Transcript of Major Histocompatibility Complex. Principles of Immune Response Highly specific recognition of...
Major Histocompatibility Complex
Principles of Immune Response
• Highly specific recognition of foreign antigens
• Mechanisms for elimination of microbes bearing such antigens
• A vast universe of distinct antigenic specifies
• Immunologic memory
• Tolerance of self-antigens
Distinct Cells in Immune System
• Lymphocytes (B cells, T cells) - Determining specificity of immunity
• Monocyte/macrophage, dendritic cells, natual killer cells and other members of myeloid cells - Antigen presentation - Mediation of immunologic functions
• Specialized epithelial and stromal cells - Providing anatomic environment
T Lymphocytes
• Helper (CD4+) and Cytotoxic (CD8+) T cells
• Help B cells develop into antibody-producing cells (HTL)
• Directly killing of target cells (CTL)
• Enhance the capacity of monocytes and macrophage
• Secretion of cytokines
Major Histocompatibility Complex (MHC)
• Transfer of information about proteins within a cell to the cell surface
• MHC I are expressed on the great majority of cells and recognized by CD8+ T cells
• MHC II are expressed on B cells, macrophages, dendritic cells and recognized by CD4+ T cells
• Responsible for graft rejection• Found on chromosome 6 in human and 17 in
mouse
Antigen Presentation Pathways
TCR/peptide-MHC Complex
T Cell Activation
One Receptors, Two Kinds of Signals
X-ray Crystal Structures
Peptides Binding to MHC Molecules
• MHC I molecules bind short peptides, usually between 8 and 10 residues.
• The typical length of a class I ligand comprises 9 amino acids.
• Class II ligands consist of 12 to 25 amino acids.
• A core of nine amino acids is essential for peptide/MHC binding.
MHC peptide prediction
• Understanding the basis of immunity
• Development of peptide vaccines
• Immunotherapeutics for cancer and autoimmune disease
• Several mathematical approaches for MHC peptide binding prediction
Binding Motifs
• Hammer et al., 1993; Hammer, 1995; Rammensee et al., 1995; Sette et al., 1989
• Specify which residues at given positions within the peptide are necessary or favorable for binding to a specific MHC molecule.
Quantitative Matrices (QM)
• Parker et al., 1994
• Dominant anchor residues
- Leu or Met at P2, and Val or Leu at P9
• Auxiliary anchor residues
• Assumed the stability contributed by a given residue at a given position is independent of the sequence of the peptide
QM – Error Function
• Data set: 154 peptides binding to HLA-A2• For a peptide, GILGFVFTL ERR = In(t1/2) –
In(G1 * I2 * L3 * G4 * F5 * V6 * F7 * T8 * L9 * Constant)
t1/2 : half-life of dissociation in minutes at 37"C
• Construct coefficients table (20 aa x 9 positions) that minimizing the sum of error functions
• Calculate theoretical dissociation rate
QM – Coefficients Table
aa Coeff Freq aa Coeff Freq aa Coeff Freq
Neural Networks (NN)
• Gulukota et al., 1997• 463 nonapeptides binding to HLA-A2.1 with IC50 • A feedforward architecture
NN - Model
The output state of any neuron i, Xi, is computed as
Wij is the weight of the connection from neuron j to neuron i.
g is the sigmoidal function, g(x) = 1/(1 + e-x).
Desired (target) output of the net for a peptide is
NN – Performance
• Training set: 146 peptides• Test set: 317 peptides• Border is defined as 500 nM
NN – Performance
• Sensitivity =
TP/(TP+FN)• Specificity =
TN/(TN+FP)• Positive Prediction
Value =
TP/(TP+FP)• Negative
Prediction Value =
TN/(TN+FN)
Support Vector Machines (SVM)
• Dönnes and Elofsson, 2002• Input Vector
- amino acid sequence,
- binder/non-binder,• Constructing the hyperplane with the maximum distance
to the nearest data points of each class in the feature space.
• Linear, polynomial and radial basis kernel functions were tested for prediction
,
SVM - Hyperplane
• Decision function can be written
• Maximize
subject to
MHC Peptide DB - MHCPEP
• Brusic et al. 1998 • Comprising over 13000 peptide
sequences known to bind MHC molecules• Entries are compiled from published
reports as well as from direct submissions of experimental data.
• Containing peptides that have been reported to bind to MHC in the absence of any functional data
MHC Peptide DB - SYFPEITHI
• Rammensee et al., 1999
• Naming: First MHC-eluted peptide that was directly sequenced (Falk et al. 1991).
• Restricted to published data
• Only contain sequences that are natural ligands to T-cell epitopes
• Comprising more than 4000 entries
SVMHC - Performance
• Prediction for 6 MHC types using SYFPEITHI data for SVM training
• Prediction for 26 MHC types using MHCPEP data for SVM training
MHC Peptide DB - SYFPEITHI
• 10: frequently occur in anchor positions
• 8: a significant number of ligands
• 6: rarely occurring residues
• 4: less frequent residues of the same set
• 1-4: preferred, according to the strength
• -1 to -3: unfavorable for binding
Servers for peptide-MHC binding