New Approaches to in silico Design of Epitope-Based Vaccines

Chapter 8
Discussion & Conclusion
Parts of this chapter have previously been published [8].
Traditional trial-and-error based approaches to vaccine design have been remarkably
successful. One of the major successes was the eradication of smallpox in the 1970s.
However, there are still many diseases for which no viable vaccine could be found, HIV
infection and cancer being among the most prominent examples. Here, new, rationally
designed types of vaccines, as, e.g., EVs, are promising alternatives.
The process of designing an EV can roughly be divided into three steps: epitope discovery,
epitope selection and epitope assembly. In silico epitope discovery has the potential
to drastically reduce the number of biological experiments that have to be performed. A
key step in in silico approaches to epitope discovery is the prediction of MHC-binding peptides.
This problem has been tackled with the full range of possible mathematical methods.
Mostly machine learning methods have proven useful. However, the prediction of binders
for MHC alleles with little or no experimental data is problematic. In Sections 4.2 and 4.3
we present two approaches to overcome the problem of scarcity of data. Our first approach
improves the predictive power of SVMs for alleles with little experimental binding data by
combining the benefits of string kernels with the ones of physicochemical descriptors for
AAs. Our second approach, UniTope, for the first time allowed predictions for all MHC-I
alleles. This was achieved by employing all available MHC-I binding data for the prediction
of an individual allele’s binding specificity. The kernels developed in our first approach
are particularly useful when data is scarce. Next to allele-specific MHC binding prediction
they promise to be beneficial in, e.g., the prediction of substrate specificities within enzyme
families [151]. Use of these kernels in UniTope (Section 4.3), however, will not yield further
improvements: Due to the pooling of all available binding data, training data is far from
being scarce in this setting.
Pan-specific approaches like UniTope have contributed significantly to the advancement
of in silico epitope discovery. Nevertheless, it has to be noted that there is a drawback
to current approaches: for a considerable fraction of alleles pan-specific models perform
worse than allele-specific models, i.e., inclusion of knowledge on other alleles’ binding
specificities is disadvantageous. Here, models trained for a specific allele incorporating a
limited number of examples from highly related other alleles [152] show promise. However,
this approach brings about another drawback: when confronted with a new MHC allele a
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