Supplementary Figure 7: Training of MARIA on HLA-DQ ligands and gluten peptide deamination effects on HLA-DQ presentation.
From: Predicting HLA class II antigen presentation through integrated deep learning
(a) Overlap of HLA-DQ2.2 and HLA-DQ2.5 peptide ligands. Ligands from these two alleles overlap 29% when counting identical peptide sequences only. (b) Training, validation, test of MARIA models for HLA-DQ2.2 presentation. To train the MARIA DQ2.2 model, 5845 peptides shared between HLA-DQ2.2 and HLA-DQ2.5, and 2529 peptide unique to HLA-DQ2.2 were used as the positive examples; 8374 length-matched peptides were used as negative examples. Peptide sequences were assigned into training, validation, and test set. No peptides in validation and test set were substring of a training peptide, vice versa. (c) MARIA predicted presentation scores on HLA-DQ2.2 presentation of five known celiac disease related gluten peptides upon all possible Q->E or Q->K mutations. Based on MARIA-DQ ranks, deamination forms (Q->E) of gluten peptides present better compared to unmodified forms or Q -> K forms of gluten peptides (* indicates p=3e-4, ** = P<1e-5, Mann-Whitney U test, n=15, 255, 7, 31, 63).
