Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions
Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions
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DOI：
10.4049/jimmunol.1600808
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发表时间：
2016-09-15
影响因子：
4.4
通讯作者：
Gfellert, David
中科院分区：
文献类型：
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作者：
Bassani-Sternberet, Michal;Gfellert, David
HLA分子上的抗原提呈在传染病和肿瘤免疫学中发挥着核心作用。迄今为止，从DNA测序数据中大规模鉴定（新）抗原主要依赖于预测。同时，越来越多地进行HLA肽组的质谱分析以直接检测HLA分子上呈递的肽。在这项研究中，我们使用一种新的无监督的方法来分配基于质谱的HLA肽组数据，其同源HLA分子。我们表明，在配体预测算法中加入去卷积的HLA肽组数据可以提高现有数据库中配体较少的HLA等位基因的准确性。我们对自然加工的HLA肽的大数据集的计算分析的结果，以及实验验证和蛋白质结构分析，进一步揭示了HLA结合基序如何随肽长度变化，并预测了HLA-B 07：02配体中遥远残基之间的新的合作效应。
Ag presentation on HLA molecules plays a central role in infectious diseases and tumor immunology. To date, large-scale identification of (neo-)Ags from DNA sequencing data has mainly relied on predictions. In parallel, mass spectrometry analysis of HLA peptidome is increasingly performed to directly detect peptides presented on HLA molecules. In this study, we use a novel unsupervised approach to assign mass spectrometry-based HLA peptidomics data to their cognate HLA molecules. We show that incorporation of deconvoluted HLA peptidomics data in ligand prediction algorithms can improve their accuracy for HLA alleles with few ligands in existing databases. The results of our computational analysis of large datasets of naturally processed HLA peptides, together with experimental validation and protein structure analysis, further reveal how HLA-binding motifs change with peptide length and predict new cooperative effects between distant residues in HLA-B07:02 ligands.