New Approaches to in silico Design of Epitope-Based Vaccines

94 CHAPTER 8. DISCUSSION & CONCLUSION
new model has to be trained, which can be very time consuming. Making such a method
publicly available, e.g., via a web server, to offer predictions for all known MHC alleles
is hence inapplicable. Alternative approaches are required and can be expected to come
from the field of transfer learning. Transfer learning deals with transferring knowledge
from one task to another task. It is a hot topic in the machine learning community [153–
155]. Increasingly sophisticated transfer learning methods can be expected to improve
pan-specific MHC binding prediction while at the same time being able to handle the
increasing amounts of MHC binding data.
Today, despite minor shortcomings, the problem of predicting whether or how strongly
a peptide binds to the product of a given MHC allele can be considered as solved. However,
this is not the case for epitope discovery in general. The actual prediction of T-cell epitopes,
i.e., to distinguish immunogenic from non-immunogenic MHC binders, is still in its infancy.
It will be one of the key issues for immunoinformatics to address in the near future.
Due to the complex host dependencies accounting for the T-cell recognition of a pMHC
complex, the problem is particularly challenging. Previous methods do not consider these
dependencies but utilize peptide sequence information only yielding limited prediction
accuracies. In Section 4.4 we present a novel approach to T-cell epitope prediction that
takes a systemic view on the problem. Evaluation on a small unbiased and allele-specific
data set showed promising results. While the small sample allows demonstration of a proofof-concept,
a more thorough analysis of the individual aspects of our approach requires
more data.
Reliable immunogenicity prediction is a prerequisite for fully rational EV design. The
move from simple sequence-based predictors to predictors including system-wide properties
promises to be a step in the right direction. However, in order for T-cell epitope prediction
to eventually come of age, sufficiently large and consistent data sets for various alleles
need to become publicly available. Here, the development of high-throughput methods to
experimentally identify T-cell epitopes [156] will play an important role.
In this thesis, we only consider naturally occurring peptides. There have also been studies
successfully using epitope analogs, namely fixed anchor and heteroclitic epitopes [25].
While fixed anchor approaches aim at increasing pMHC binding affinity by modification of
MHC anchor residues, the heteroclitic epitopes approach aims at increasing pMHC:TCR
affinity by modification of TCR interaction sites. The capability of such epitope analogs to
induce more potent immune responses than the corresponding wild-type epitopes as well
as to break T-cell tolerance has been shown in several studies [157, 158]. For the design of
epitope analogs, it would be desirable to have accurate prediction methods that complement
their prediction with information, for example, on what AA properties are important
for a specific position. Such information cannot be easily retrieved from state-of-the-art
prediction methods. It has to be noted that the use of modified epitopes is controversial.
Induction of T-cell populations with low recognition efficiencies [159] and the appearance
of cryptic epitopes with dominant immunogenicity [160] have been reported.
In EV design, epitope discovery is followed by epitope selection. Out of a given set of
candidate epitopes, the vaccine epitopes have to be selected. Depending on the requirements
on the desired epitope set, this task can become highly complex, rendering manual