HLAIIPred: cross-attention mechanism for modeling the interaction of HLA class II molecules with peptides.

ABSTRACT: We introduce HLAIIPred, a deep learning model to predict peptides presented by class II human leukocyte antigens (HLAII) on the surface of antigen presenting cells. HLAIIPred is trained using a Transformer-based neural network and a dataset comprising of HLAII-presented peptides identified by mass spectrometry. In addition to predicting peptide presentation, the model can also provide important insights into peptide-HLAII interactions by identifying core peptide residues that form such interactions. We evaluate the performance of HLAIIPred on three different tasks, peptide presentation in monoallelic samples, immunogenicity prediction of therapeutic antibodies, and neoantigen prioritization for cancer immunotherapy. Additionally, we created a dataset of biotherapeutics HLAII peptides prese

SUBMITTER: Haghighatlari M 

PROVIDER: S-EPMC12310933 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature