  Major histocompatibility complex class two (MHC-II) molecules are
trans-membrane proteins and key components of the cellular immune system. Upon
recognition of foreign peptides expressed on the MHC-II binding groove, helper
T cells mount an immune response against invading pathogens. Therefore,
mechanistic identification and knowledge of physico-chemical features that
govern interactions between peptides and MHC-II molecules is useful for the
design of effective epitope-based vaccines, as well as for understanding of
immune responses. In this paper, we present a comprehensive trans-allelic
prediction model, a generalized version of our previous biophysical model, that
can predict peptide interactions for all three human MHC-II loci (HLA-DR,
HLA-DP and HLA-DQ), using both peptide sequence data and structural information
of MHC-II molecules. The advantage of this approach over other machine learning
models is that it offers a simple and plausible physical explanation for
peptide-MHC-II interactions. We train the model using a benchmark experimental
dataset, and measure its predictive performance using novel data. Despite its
relative simplicity, we find that the model has comparable performance to the
state-of-the-art method. Focusing on the physical bases of peptide-MHC binding,
we find support for previous theoretical predictions about the contributions of
certain binding pockets to the binding energy. Additionally, we find that
binding pockets P 4 and P 5 of HLA-DP, which were not previously considered as
primary anchors, do make strong contributions to the binding energy. Together,
the results indicate that our model can serve as a useful complement to
alternative approaches to predicting peptide-MHC interactions.
