A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity - Xu, Shi; Wang, Xiaohua; Fei, Caiyi (Frontiers in Oncology, 2022)
In the past decade, the substantial achievements of therapeutic cancer vaccines have shed a new light on cancer immunotherapy. The major challenge for designing potent therapeutic cancer vaccines is to identify neoantigens capable of inducing sufficient immune responses, especially involving MHC-II epitopes. However, most previous research on T cell epitopes focused on either ligand binding or antigen presentation by MHC rather than the immunogenicity of T cell epitopes. In order to better facilitate therapeutic vaccine design, in this study we propose a revolutionary new tool: a convolutional neural networks model named FIONA (Flexible Immunogenicity Optimization Neural-network Architecture) trained on IEDB datasets. FIONA could accurately predict the epitopes presented by given specific MHC-II subtypes, as well as their immunogenicity. By leveraging HLA allele hierarchical encoding model together with peptide dense embedding fusion encoding, FIONA (with AUC = 0.94) outperforms several other tools in predicting epitopes presented by MHC-II subtypes in head-to-head comparison; moreover, FIONA has unprecedentedly incorporated the capacity to predict the immunogenicity of epitopes with MHC-II subtype specificity. Therefore, we developed a reliable pipeline to effectively predict CD4+ T cell immune responses against cancer and infectious diseases.