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Major Histocompatibility Complex

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.

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Major Histocompatibility Complex

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  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(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

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

  17. Neural Networks (NN) • Gulukota et al., 1997 • 463 nonapeptides binding to HLA-A2.1 with IC50 • 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. 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

  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. 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

  27. Servers for peptide-MHC binding

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