Presentation is loading. Please wait.

Presentation is loading. Please wait.

Major Histocompatibility Complex. Principles of Immune Response Highly specific recognition of foreign antigens Mechanisms for elimination of microbes.

Similar presentations


Presentation on theme: "Major Histocompatibility Complex. Principles of Immune Response Highly specific recognition of foreign antigens Mechanisms for elimination of microbes."— Presentation transcript:

1 Major Histocompatibility Complex

2 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

3 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

4 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

5 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

6 Antigen Presentation Pathways

7 TCR/peptide-MHC Complex

8 T Cell Activation

9 One Receptors, Two Kinds of Signals

10 X-ray Crystal Structures

11 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.

12 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

13 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.

14 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

15 QM – Error Function Data set: 154 peptides binding to HLA-A2 For a peptide, GILGFVFTL ERR = In(t 1/2 ) – In(G1 * I2 * L3 * G4 * F5 * V6 * F7 * T8 * L9 * Constant) t 1/2 : 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

16 QM – Coefficients Table aa Coeff Freq aa Coeff Freq aa Coeff Freq

17 Neural Networks (NN) Gulukota et al., nonapeptides binding to HLA-A2.1 with IC 50 A feedforward architecture

18 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

19 NN – Performance Training set: 146 peptides Test set: 317 peptides Border is defined as 500 nM

20 NN – Performance Sensitivity = TP/(TP+FN) Specificity = TN/(TN+FP) Positive Prediction Value = TP/(TP+FP) Negative Prediction Value = TN/(TN+FN)

21 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,

22 SVM - Hyperplane Decision function can be written Maximize subject to

23 MHC Peptide DB - MHCPEP Brusic et al Comprising over 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

24 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

25 SVMHC - Performance Prediction for 6 MHC types using SYFPEITHI data for SVM training Prediction for 26 MHC types using MHCPEP data for SVM training

26

27 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

28 Servers for peptide-MHC binding


Download ppt "Major Histocompatibility Complex. Principles of Immune Response Highly specific recognition of foreign antigens Mechanisms for elimination of microbes."

Similar presentations


Ads by Google