MHC molecules are cell surface glycoproteins, which take active part in host
immune reactions. The counterparts in human are known as HLA (Human Leukocyte
Antigens). The discovery of MHC molecules is related to transplantation rejection.
Their functions were unraveled by experiments in inbred and congenic mice
The MHC molecules
are coded by three classes of genes. Class I and Class II gene products are
directly associated with immune reactions whereas Class III gene products
play an indirect role.
Class I genes
encodes the principle subunits of MHC I glycoprotein called H2-K, H2-d, H2-l
in mice and HLA- A, B, C in humans. Proteins encoded by these genes are present
virtually on all nucleated cells. Class I molecules consists of a heavy peptide
chain of 43kDa non-covalently bonded to a smaller 11 kDa fragment called
beta-2 microglobulin. The largest part of the heavy chain is organized into
three globular domains (alpha-1, 2 and 3 ) which protrudes from the cell
membrane. A hydrophobic segment of alpha chain anchors the molecule to the
membrane. X-ray analysis has provided an exciting leap forward in our understanding
of MHC functions. Both beta-2 microglobulin and alpha-3 domain resembles
classical immunoglobulin fold. However, alpha-1 and alpha-2 domains form
an utterly surprising structure composed of two extended alpha helices, above
a floor created by peptide strands held together in a beta pleated sheet.
These proteins, which elicit an intense response of CD8+ T cells, play a
major role in graft rejection or infected cell clearance.
Class II genes
encodes cell surface glycoproteins which are structurally very similar to
MHC Class I molecules. These molecules are expressed only on Antigen Presenting
Cells (APC). Together with antigenic fragments, the Class II proteins form
epitopes that are recognized by T-helper cells (CD4+). Hence MHC Class II
proteins are critically involved in response to nearly all antigens.
genes encode three proteins of The Complement Cascade (C2, C4 and Bf) and
two cytotoxic proteins (TNF and lymphotoxin). These proteins are involved
in diverse immune reactions, directly or indirectly.
and binding studies revealed similar conformation of peptide ligands bound
to both Class I & II molecules. Class I molecules interact with
the N- and C- terminals of the bound peptide, leaving a bulge in the middle.
These N- & C- terminal interactions together with closed peptide binding
groove restricts the length of interacting peptide to 8-10 amino acids. However
peptide binding groove of Class II molecules is open at both ends and the
interactions of peptide are more diffuse thereby a more variable length is
allowed (generally 10 -28 amino acids). The involvement of MHC class-II in
response to almost all antigens and the variable length of interacting peptides
makes the study of MHC Class II molecules very interesting. MHC molecules
have been well characterized in terms of their role in immune reactions.
They bind to some of the peptide fragments generated after proteolytic cleavage
of antigen. This binding acts like red flags for antigen specific T-cells
to generate immune response against the parent antigen. So a small fragment
of antigen can induce immune response against whole antigen. This theme is
implemented in designing subunit and synthetic peptide vaccines. The question
that remained unanswered in this context was "How to identify the regions
which can bind to MHC and evoke a T cell response ?".
way is to scan the whole antigen sequence by synthesizing overlapping
peptide fragments and assaying for immune reactions. Though the technique
is 100% accurate but it requires lot of time and is expensive. A better alternative
is to restrict the number of peptides required for scanning. This is where
the prediction methods come into play.
which still pose questions in the development of an efficient prediction
method are " The same MHC molecule can bind a range of peptides" and "MHC
allelic polymorphism". So overall the situation is that we have many alleles
of MHC molecules, each of which can bind to a wide range of peptides. Researchers
have tried to answer these questions by simply asking question " Is there
any set of amino acids responsible for specific binding to MHC molecules ?"
. The answer to this question gave the MHC binding peptide prediction
MHC binding peptide prediction methods can be divided into three main groups
a) Motif based methods, b) Statistical/ Mathematical expression based methods
and, c) Structure based methods.
describe general position based patterns of recurrent amino acids favorable
for HLA- peptide binding. Prediction methods based on binding motifs are
mostly all or none algorithms with high false rates.
Mathematical expression based methods include Quantitative matrix and Neural
network based methods. Quantitative matrices provide a linear model with
easy to implement capabilities. Their predictive accuracies are considerable.
On the other hand, neural networks are more complex, nonlinear and self learning
systems. Their predictive accuracies are higher but they require large amount
of data for learning which makes Quantitative matrix based methods suitable
for MHC binding peptide predictions.
based methods are logically very sound but computationally complex. These
methods calculate binding energy of peptide-MHC complex and the energetically
favorable peptides are predicted as binders. These methods are in stages
All the above
mentioned approaches cannot effectively deal with MHC Polymorphism i.e. for
each allele a separate matrix has to be generated or a separate set of rules
have to be applied. Recently, Sturinolo et al., 1999
provided an answer by using virtual matrix which holds promise for delivering
better MHC BINDING PEPTIDE PREDICTION METHOD.