IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe Genesis, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. Genesis comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, Genesis' encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. Genesis outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve=0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. Genesis shows further improved performance on immunogenicity prediction with the integration of TCR context information. Genesis performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets.

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

Givechian,

Rocha,

Yang

et al. 2024

Preprint

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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∼27,000 peptide-MHC complexes that we generated with AlphaFold, 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 within vitroassay results for a set of SARS-CoV-2 epitopes. This work also presents a new architecture that incorporates equivariant graph processing and multi-modal data integration for the long standing task in immunotherapy.

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IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Cited by 3 publications

References 80 publications

Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab

Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab

Genesis: A modular protein language modelling approach to immunogenicity prediction

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

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