New Approaches to in silico Design of Epitope-Based Vaccines

prediction of T-cell epitopes is a rather challenging problem. However, the capability of a
peptide to induce a T cell-mediated immune response, i.e., its immunogenicity, has been
shown to correlate well with MHC binding affinity [9]: most high-affinity peptides seem to
be immunogenic. Since the processes governing pMHC binding are well-understood, the
epitope discovery problem is typically reduced to the problem of MHC binding prediction.
MHC Binding Prediction
In 2004, Singh-Jasuja et al. presented the Tübingen approach [10] to acquire an experimentally
validated initial list of epitopes from tumor-associated antigens. In their work, the
incorporation of computational methods for prediction of pMHC binding in the process is
proposed. Since such prediction methods help to reduce the number of experiments to be
performed, they have become standard tools in immunology. Most popular approaches are
either matrix-based [11, 12] or use machine learning techniques [13, 14]. Typical matrixbased
approaches assume that each side chain of a peptide contributes independently to
the peptide’s binding affinity. In contrast, most machine learning methods are capable of
capturing nonlinear correlations. Hence, the resulting models are generally more accurate.
Their construction does, however, require considerable amounts of data.
A major challenge in MHC binding prediction is the highly polymorphic nature of
the MHC. As of today, 4,946 MHC-I and 1,457 MHC-II alleles are known (IMGT/HLA
database [15, 16], release 3.4.0). Different allelic variants of MHC molecules bind and
present different sets of peptides. Hence, the MHC ligandome varies from individual to
individual. This has important implications for vaccine design: because T cells recognize
immunogenic peptides only when bound to MHC molecules, a peptide capable of inducing
a T cell-mediated immune response in one individual might not be capable of doing so in
another. Moreover, within a population, some MHC alleles are more common than others
and the allele distribution differs between populations.
Classical prediction methods develop allele-specific models and require a minimum of
experimental binding data for the respective allele. For the majority of all known MHC
alleles the amount of available data is insufficient. However, to address the challenges of
vaccine design, the binding specificities the gene products of all MHC alleles need to be
known. It is thus crucial to find a way to overcome the problem caused by the lack of data.
We propose two approaches to overcome this problem and thus to improve MHC-I
binding prediction. Both approaches employ support vector machines (SVMs), a powerful
and very popular machine learning technique. Given a sufficiently large set of examples,
e.g., MHC binders and MHC non-binders, an SVM detects patterns within the examples
and, based on these, learns to make intelligent decisions regarding unseen data. In order
to do so, SVMs require so-called kernel functions or kernels, which measure the similarity
between examples.
Our first approach to address the problem of scarce data focuses on improving the predictive
power of SVMs for alleles with little experimental binding data by more skillfully
exploiting the available information. MHC molecules bind peptides in an extended conformation.
Within the complex the peptide’s side chains interact with surrounding side
chains of the MHC and also with each other. Each of the peptide’s side chains contributes
3