Improving generalizability and data efficiency for MHC-I binding peptide predictions through structure-based geometric deep learning

Abstract

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal for tissue transplantation, pathogen recognition and autoimmune disease treatments. Recent advances in cancer immunotherapies demand for more accurate computational prediction of MHC-bound peptides. We address the generalizability challenge of MHC-bound peptide predictions, revealing limitations in current sequence-based approaches. Our solution employs structure-based methods leveraging geometric deep learning (GDL), yielding up to 8% improvement in generalizability across unseen MHC alleles. We tackle data efficiency by introducing a self-supervised learning approach surpassing sequence-based methods, even without being trained on binding affinity data. Finally, we demonstrate the resilience of structure-based GDL methods to biases in binding data on an Hepatitis B virus vaccine design case study. This study highlights structure-based methods’ potential to enhance generalizability and data efficiency, with implications for data-intensive fields like T-cell receptor specificity predictions, paving the way for enhanced comprehension and manipulation of immune responses.