人类白细胞抗原(HLA)介导的免疫反应的现代疫苗设计和研究在很大程度上依赖于HLA等位基因特异性结合基序的知识和HLA肽结合亲和力的计算预测。HLA肽的突破大大扩展了天然HLA配体的数据库,并实现了HLA肽结合特异性的详细表征。然而,分析HLA肽组学数据时必须注意,因为鉴定的肽可能是质谱分析中的污染物,或者可能与HLA分子弱结合.这里,我们将杂交从头肽测序方法应用于大规模单等位基因HLA肽组学数据集,以发现新的配体并完善HLA结合基序的现有知识.高达12-40%的肽组学数据是在C末端具有精氨酸或赖氨酸的低结合亲和力肽,并且可能是胰蛋白酶肽污染物。已经在社区数据库中报道了数千种这些肽作为合法配体,并且可能被错误地用于训练预测模型。此外,鉴定的配体的无监督聚类揭示了几个HLAI类等位基因的其他结合基序和有效分离的异常值,这些异常值被实验证实为假阳性。总的来说,我们的研究结果扩展了HLA结合特异性的知识,并主张对HLA肽组学数据进行更严格的解释,这将确保社区HLA配体组数据库的高有效性.
Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.