New Approaches to in silico Design of Epitope-Based Vaccines

48 CHAPTER 4. EPITOPE DISCOVERY
Figure 4.7: Data set composition. The distribution of NetMHC scores in the immunogenic
and non-immunogenic examples of (A) the original set of EBV ninemers, D, and (B) the subset
selected for training, ˜ D, is displayed.
reported within this subsection are averaged over 100 runs of two-times nested five-fold
cross validation to reduce random fluctuations of the performance.
Sequence-Based Predictions
For the prediction of T-cell epitopes based on sequence information only, we use SVC
with a Gaussian RBF kernel on blosum50-encoded peptide sequences. The mean auROC
is 0.72. As a comparison, an approach based on POPI, i.e., SVC with a Gaussian RBF
kernel on physicochemically encoded peptide sequences, achieves a mean auROC of 0.69.
Employment of a WD kernel as in POPISK yields a mean auROC of 0.70.
Incorporation of Self-Tolerance
Incorporation of self-tolerance is achieved by adding sequence-based and self-tolerancebased
kernels. The self-tolerance-based kernel is a Gaussian RBF kernel on the 201dimensional
feature vectors encoding self-tolerance information with respect to one of
the three reference proteomes: thymus-min, thymus-max or IPI. While incorporation of
the thymus-max-based self-tolerance information resulted in a considerable improvement
(auROC = 0.78), this was not the case for the IPI- and thymus-min-based self-tolerance information
(auROCs of 0.70 and 0.71, respectively). This is consistent with the performance
of purely self-tolerance-based predictors: the thymus-max-based predictor (auROC = 0.58)
clearly outperforms the IPI- (auROC = 0.48) and thymus-min-based (auROC = 0.44) predictors.