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T cell epitope prediction has always been an important research
topic in immunotherapy and vaccine research. Previously Dr. Darren
Flower and Dr. Irini Doytchinova developed a computational
algorithm named the additive method to predict T cell
epitopes. The additive method is derived from Free-Wilson
approach, which assumes that each substituent makes an additive
and independent contribution to the biological activity. The
additive method considers three types of interactions: the
interaction between individual amino acids and the binding site,
the interaction between adjacent and every second amino acids and
their effects on binding. A number of allele specific models have
been developed in Dr. Darren Flower's group since, including both
human and mouse models. The additive method has been implemented
into a web server named MHCPred, which can be used for online T
cell epitope prediction. The web server EpiJen is a further
development of the additive approach. There are many factors in
epitope recognition and not every peptide with high affinity to
MHC proteins are epitopes. We try to model several important stage
of the MHC degradation pathway: proteasome cleavage, TAP binding
and MHC binding. The process is summerised in figure 1.
Figure 1. A flow chart of EpiJen prediction process.
The result output from EpiJen often consists of a very small
number of peptides, eg. 5%. We believe that the predictivity of
EpiJen is better than other T cell epitope prediction algorithms
as it also considers proteasome cleavage and TAP binding,
therefore it is able to mimic the MHC binding machnism in real
life. At present only HLA class I alleles are included in the
prediction. We aim to include a wider range of models as research
progresses.
Publication:
Doytchinova, I. A.,
P. Guan, D. R. Flower. EpiJen: a server for multi-step T cell
epitope prediction. BMC
Bioinformatics, 2006,
7, 131. |
