Updated publication reference for PubMed record(s): 31848261. The identification and prediction of HLA-I–peptide interactions play an important role in our understanding of antigen recognition in infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. In this study, we collected in-depth phosphorylated HLA-I peptidomics data (1,920 unique phosphorylated peptides) from several studies covering 67 HLA-I alleles and expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands for several alleles. In addition to the previously observed preferences for phosphorylation at P4, for proline next to the phosphosite and for arginine at P1, we could detect a clear enrichment of phosphorylated peptides among HLA-C ligands and among longer peptides. Binding assays were used to validate and interpret these observations. We then used these data to develop the first predictor of HLA-I– phosphorylated peptide interactions and demonstrated that combining phosphorylated and unmodified HLA-I ligands in the training of the predictor led to highest accuracy.