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Major Histocompatibility Complex. Kung-yen Chang System Biology, 2003. 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

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major histocompatibility complex

Major Histocompatibility Complex

Kung-yen Chang

System Biology, 2003

principles of immune response
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

System Biology

distinct cells in immune system
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

System Biology

t lymphocytes
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

System Biology

major histocompatibility complex mhc
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

System Biology

antigen presentation pathway mhc i
Antigen Presentation Pathway – MHC I
  • Intracellular antigens
  • Viruses

System Biology

antigen presentation pathway mhc ii
Antigen Presentation Pathway – MHC II
  • Extracellular antigens
  • Bacteria and Parasites

System Biology

t cell activation
T Cell Activation

System Biology

peptides binding to mhc molecules
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.

System Biology

mhc peptide prediction
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

System Biology

binding motifs
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.

System Biology

quantitative matrices qm
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

System Biology

qm error function
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

System Biology

qm coefficients table
QM – Coefficients Table

aa Coeff Freq aa Coeff Freq aa Coeff Freq

System Biology

neural networks nn
Neural Networks (NN)
  • Gulukota et al., 1997
  • 463 nonapeptides binding to HLA-A2.1 with IC50
  • A feedforward architecture

System Biology

nn model
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

System Biology

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

System Biology

nn performance22
NN – Performance
  • Sensitivity =


  • Specificity =


  • Positive Prediction Value =


  • Negative Prediction Value =


System Biology

support vector machines svm
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


System Biology

svm hyperplane
SVM - Hyperplane
  • Decision function can be written
  • Maximize

subject to

System Biology

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

System Biology

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

System Biology

svmhc performance
SVMHC - Performance
  • Prediction for 6 MHC types using SYFPEITHI data for SVM training
  • Prediction for 26 MHC types using MHCPEP data for SVM training

System Biology

mhc peptide db syfpeithi29
  • 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

System Biology