Predicting Class I Major Histocompatibility Complex (MHC) Binders Using Multivariate Statistics: Comparison of Discriminant Analysis and Multiple Linear Regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based
vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work.
Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized
by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex
(MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder.
Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study,
we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of
quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the
well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two
methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external
set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.