Linear vs. non-linear dimensionality reduction techniques in predicting class-II MHC peptide binding

A key step in the development of an adaptive immune response to vaccines is the binding of peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated. Several algorithms have been proposed for such binding predictions, but are limited to a small number of MHC molecules and present imperfect prediction power. We are undertaking an exploration of the power gained by taking advantage of a natural representation of the protein sequence amino acid in terms of their composition, structural and a series of associated physicochemical properties to form a representative descriptor vectors. We are proposing to use dimensionality reduction techniques to preprocess the descriptor vectors before feeding them into well known statistical classifiers for binding prediction. In all cases, coupling dimensionality reduction techniques with the physicochemical properties leads to substantially higher values for our evaluation criteria (Area Under ROC Curve) which means that misclassification errors is reaching lower rates.