ImmunoStruct: integration of protein sequence, structure, and biochemical properties for immunogenicity prediction and interpretation

Kevin Bijan Givechian, João Felipe Rocha, Edward Yang, Chen Liu, Kerrie Greene, Rex Ying, Etienne Caron, Akiko Iwasaki, Smita Krishnaswamy

Abstract: Epitope-based vaccines are promising therapeutic modalities for infectious diseases and cancer, but identifying immunogenic epitopes is challenging. The vast majority of prediction methods are sequence-based, and do not incorporate wide-scale structure data and biochemical properties across each peptide-MHC (pMHC) complex. We present ImmunoStruct, a deep-learning model that integrates sequence, structural, and biochemical information to predict multi-allele class-I pMHC immunogenicity. By leveraging a multimodal dataset of $\sim$27,000 peptide-MHC complexes we demonstrate that ImmunoStruct improves immunogenicity prediction performance and interpretability beyond existing methods, across infectious disease epitopes and cancer neoepitopes. We further show strong alignment with in vitro assay results for a set of SARS-CoV-2 epitopes, as well as strong performance in pMHC-based cancer patient survival prediction. Overall, this work also presents a new architecture that incorporates equivariant graph processing and multimodal data integration for the long standing task in immunotherapy.