Deep Learning-Based Motif Discovery in MajorHistocompatibility Complex: A Primer

In this work, we present a deep learning-based approach for motif discovery in the Major Histo-compatibility Complex (MHC) system, which plays a key role in the immune response. We explore the use of convolutional neural networks (CNNs) to identify peptide binding motifs for different MHC alleles. By training models on data from specific MHC Class I and II molecules, we demonstrate how 1-dimensional convolutional filters can effectively capture motifs and binding preferences. Our study introduces a method for extracting motif logos directly from the trained models, providing insights into how internal neural network representations align with known biological motifs. The results show significant alignment with experimental binding motifs, underscoring the utility of deep learning in immunological research and the potential for improving vaccine design and immunotherapy.