  The major histocompatibility complex (MHC) class-I pathway supports the
detection of cancer and viruses by the immune system. It presents parts of
proteins (peptides) from inside a cell on its membrane surface enabling
visiting immune cells that detect non-self peptides to terminate the cell. The
ability to predict whether a peptide will get presented on MHC Class I
molecules helps in designing vaccines so they can activate the immune system to
destroy the invading disease protein. We designed a prediction model using a
BERT-based architecture (ImmunoBERT) that takes as input a peptide and its
surrounding regions (N and C-terminals) along with a set of MHC class I (MHC-I)
molecules. We present a novel application of well known interpretability
techniques, SHAP and LIME, to this domain and we use these results along with
3D structure visualizations and amino acid frequencies to understand and
identify the most influential parts of the input amino acid sequences
contributing to the output. In particular, we find that amino acids close to
the peptides' N- and C-terminals are highly relevant. Additionally, some
positions within the MHC proteins (in particular in the A, B and F pockets) are
often assigned a high importance ranking - which confirms biological studies
and the distances in the structure visualizations.
