Shift-Invariant Adaptive Double Threading: Learning MHC II–Peptide Binding

The major histocompatibility complex (MHC) plays important roles in the workings of the human immune system. Specificity of MHC binding to peptide fragments from cellular and pathogens' proteins has been found to correlate with disease outcome and pathogen or cancer evolution. In this paper we propose a novel approach to predicting binding configurations and energies for MHC class II molecules, whose epitopes are generally predicted less well than the MHC I epitopes due in part to larger variation in bound peptide length. We treat the relative position of the peptide as a hidden variable, and model the ensemble of different binding configurations, rather than use a separate alignment procedure to narrow it down to one. Thus, our predictor infers a distribution over peptide positions from the MHC II and peptide sequences, and computes the total binding affinity. The training procedure iterates the predictions with re-estimation of the parameters of the binding groove model. For a given relative peptide position, any MHC class I prediction model can be used. Here we choose the physics based model of Jojic et al. (2006). We show that the parameters of the binding model can be learned efficiently from the training data and then used to estimate binding energies for previously untested peptides. Our technique performs on par with previous approaches to MHC II epitope prediction. Furthermore, our model choice allows generalization to new MHC class II alleles, which were not a part of the training set.