TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides

The intricate interaction between major histocompatibility
complexes
(MHCs) and antigen peptides with diverse amino acid sequences plays
a pivotal role in immune responses and T cell activity. In recent
years, deep learning (DL)-based models have emerged as promising tools
for accelerating antigen peptide screening. However, most of these
models solely rely on one-dimensional amino acid sequences, overlooking
crucial information required for the three-dimensional (3-D) space
binding process. In this study, we propose TransfIGN, a structure-based
DL model that is inspired by our previously developed framework, Interaction
Graph Network (IGN), and incorporates sequence information from transformers
to predict the interactions between HLA-A*02:01 and antigen peptides.
Our model, trained on a comprehensive data set containing 61,816 sequences
with 9051 binding affinity labels and 56,848 eluted ligand labels,
achieves an area under the curve (AUC) of 0.893 on the binary data
set, better than state-of-the-art sequence-based models trained on
larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore,
when evaluated on the IEDB weekly benchmark data sets, our predictions
(AUC = 0.816) are better than those of the recommended methods like
the IEDB consensus (AUC = 0.795). Notably, the interaction weight
matrices generated by our method highlight the strong interactions
at specific positions within peptides, emphasizing the model’s
ability to provide physical interpretability. This capability to unveil
binding mechanisms through intricate structural features holds promise
for new immunotherapeutic avenues.