[pMHCBind: predicting major histocompatibility complex class I-peptide interactions via protein large language models and hypergraph neural networks].

Major histocompatibility complex class I (MHC I) molecules initiate CD8+ T cell immune responses by presenting antigenic peptides, a process fundamentally governed by the spatial interaction network mediated by multi-residue synergy between the binding groove and the peptide. Accurate modeling of this mechanism is crucial for tumor neoantigen screening and personalized immunotherapy. However, existing methods are largely confined to sequence-level modeling, failing to capture cross-residue higher-order structural dependencies and fine-grained binding interface interactions. To address these challenges, we propose pMHCBind, a prediction model that integrates a protein language model with a hypergraph neural network. The model employs ESM-MSA-1b and ESM-2 to extract evolution-aware residue-level embeddings for MHC I molecules and antigenic peptides, respectively, and constructs a peptide-specific hypergraph based on spatial contact relationships to model higher-order interactions via hypergraph convolution. Subsequently, cross-molecular feature fusion is employed to characterize the interaction patterns on the binding interface for presentation ability prediction. In five-fold cross-validation on the NetMHCpan-4.1 benchmark dataset and the test performance on the Anthem independent test dataset, pMHCBind consistently outperforms a variety of state-of-the-art methods across multiple metrics, including the area under receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR). Furthermore, interpretability analysis reveals that the model effectively identifies key interaction modes, such as hydrogen bonds, salt bridges, and hydrophobic interactions, between the binding groove and the peptide, demonstrating strong structural interpretability. Collectively, pMHCBind provides a promising and biologically interpretable computational tool for neoantigen recognition in precision cancer immunotherapy.