TY - JOUR
T1 - Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction
AU - Barra, Carolina
AU - Ackaert, Chloe
AU - Reynisson, Birkir
AU - Schockaert, Jana
AU - Jessen, Leon Eyrich
AU - Watson, Mark
AU - Jang, Anne
AU - Comtois-Marotte, Simon
AU - Goulet, Jean-Philippe
AU - Pattijn, Sofie
AU - Paramithiotis, Eustache
AU - Nielsen, Morten
PY - 2020
Y1 - 2020
N2 - Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.
AB - Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.
KW - MHC-II prediction
KW - Machine-learning
KW - Protein-drug immunogenicity
KW - Artificial neural-networks
KW - Immunopeptidomics
KW - Bioinformatics
U2 - 10.3389/fimmu.2020.01304
DO - 10.3389/fimmu.2020.01304
M3 - Journal article
C2 - 32655572
SN - 1664-3224
VL - 11
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 1304
ER -