A robust deep learning workflow to predict CD8 + T-cell epitopes

Genome Medicine

Research

Ricardo A. Fernandes¹  Myeongjun Jang²  Mariana Pereira Pinho³  Chloe H. Lee⁴  Paul R. Buckley⁴  Agne Antanaviciute⁴  Hashem Koohy⁵  Alison Simmons⁶  Jaesung Huh⁷ 

[1] Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, OX3 7BN, Oxford, UK;Intelligent Systems Lab, Department of Computer Science, University of Oxford, OX1 3QG, Oxford, UK;MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, OX3 9DS, Oxford, UK;MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, OX3 9DS, Oxford, UK;MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, OX3 9DS, Oxford, UK;MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, OX3 9DS, Oxford, UK;MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, OX3 9DS, Oxford, UK;Alan Turning Fellow in Health and Medicine, The Alan Turing Institute, London, UK;MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, OX3 9DS, Oxford, UK;Translational Gastroenterology Unit, John Radcliffe Hospital, OX3 9DS, Oxford, UK;Visual Geometry Group, Department of Engineering Science, University of Oxford, OX2 6NN, Oxford, UK;

关键词: Immunogenicity;    CD8 + T-cell epitopes;    Deep learning;    Transfer learning;    Computational immunology;    Epitope prediction;    Self-antigen tolerance;    MHC binding;    Thymic selection;    Neoepitope identification;    Vaccine candidates;   

DOI  :  10.1186/s13073-023-01225-z

 received in 2023-01-30, accepted in 2023-08-30,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundT-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.MethodsWe developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to ‘dissimilarity to self’ from cancer studies.ResultsTRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP.ConclusionsThis study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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