EpiMII: Integrating Structure and Graph Neural Networks for MHC-II Epitope and Neoantigen Design.

ABSTRACT: MHC-II neoantigens play a critical role in immunotherapy, either as direct effectors or through their influence on CD8⁺ T cells. However, only a small fraction of tumor DNA mutations qualify as functional neoantigens, and current prediction tools often lack accuracy, leading to the low immunogenicity of predicted neoantigens in vivo. Here, we present EpiMII, a Graph Neural Network model for MHC-II epitope design, which learns from the structural features of epitopes to predict their sequences. To train EpiMII, we constructed a reliable, large dataset containing 142,934 MHC-II epitope structures. This approach achieves a 4.2x improvement over ProteinMPNN, with a sequence recovery rate of 78.0% for known MHC-II epitopes in the Protein Data Bank. As a case study, we designed a neoa

SUBMITTER: Yuan J 

PROVIDER: S-EPMC12248154 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature