Jointly Learning to Align and Aggregate with Cross Attention Pooling for Peptide-MHC Class I Binding Prediction

Predicting binding affinities of peptide antigens presented on major histocompatibility complex (MHC) is of great importance in T-cell immune response research. Accurate prediction of peptide-MHC binding affinities is essential for vaccine design and disease treatment. Recent deep learning-based prediction methods have shown that effective sequence embedding is critical to accurately predict binding affinities. One common neural network layer shared by these methods is the global average pooling layer that aggregates features. However, can we design a better global pooling layer? Here, we introduce a novel cross attention pooling (caPool) layer to aggregate features. As our initial application of caPool, a novel end-to-end transformer model, called capTransformer, is proposed for peptide-MHC class I binding prediction. In our model, caPool jointly aligns peptide-MHC residual pairs and aggregates residual features. Thus, instead of treating all residues equally and independently, caPool focuses more on correlated residue pairs that are potentially contact pairs contributing major forces to stabilize the complex structure. Using a five-fold cross-validation experiment, we found that caPool achieved the highest PCC value of 0.845, which was 0.139 higher than a global average pooling. Here, the global pooling layer was the only difference between the two tested models, and this observation indicated that global average pooling was not always the best choice. Importantly, our capTransformer model achieved a SRCC value of 0.614 (i.e., 6.4% higher than the best-performing method) when applied to the IEDB dataset.