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

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