Extracting and Interpreting the Effects of Higher Order Sequence Features on Peptide MHC Binding
Understanding the factors contributing to peptide MHC (pMHC) affinity is critical for the study of immune responses and the development of novel therapeutics. Developments in yeast display platforms have enabled the collection of pMHC binding data for vast libraries of peptides. However, methods for interpreting this data are still at an early stage. In this work we propose an approach for extracting peptide sequence features that affect pMHC binding from such datasets. In the process we develop the theoretical framework for fitting and interpreting these features. We demonstrate that these features accurately capture the kinetics underlying pMHC binding, and can be used to predict pMHC binding well enough to rival the current state of the art. We then analyze the extracted factors and show that they correlate with our current structural understanding of MHC molecules. Finally, we discuss the implication these factors have on the complexity of peptide engineering.