Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.
Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.
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DOI：
10.1016/j.immuni.2017.02.007
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发表时间：
2017-02-21
期刊：
影响因子：
32.4
通讯作者：
Wu CJ
中科院分区：
文献类型：
--
作者：
Abelin JG;Keskin DB;Sarkizova S;Hartigan CR;Zhang W;Sidney J;Stevens J;Lane W;Zhang GL;Eisenhaure TM;Clauser KR;Hacohen N;Rooney MS;Carr SA;Wu CJ
通过液相色谱-串联质谱法（LC-MS/MS）鉴定人类白细胞抗原（HLA）结合肽有望提供对抗原呈递规则的深入理解。然而，一个关键障碍是多个HLA等位基因的共表达引起的模糊性。在这里，我们已经实施了一个可扩展的单等位基因策略来分析HLA肽组。通过使用表达单个HLA等位基因的细胞系，优化免疫纯化，并开发应用特定的光谱搜索算法，我们确定了数千种与16种不同的HLA I类等位基因结合的肽。这些数据使得亚显性结合基序的发现和量化对表位呈递至关重要的因素（例如蛋白质切割和基因表达）的贡献的综合分析成为可能。我们用我们的大数据集（> 24，000个肽）训练神经网络预测算法，并优于在具有测量亲和力的肽数据集上训练的算法。因此，我们展示了一种策略，系统地学习内源性抗原呈递的规则。HLA I类结合预测传统上是基于生物化学结合实验。Abelin及其同事提出了一种基于LC-MS/MS的工作流程和分析框架，大大加快了预测性能的提高。主要进展包括序列基序的发现和基因表达和蛋白酶体加工作用的改进量化。
Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on data-sets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation. HLA class I binding prediction has traditionally been based on biochemical binding experiments. Abelin and colleagues present an LC-MS/MS-based workflow and analytical framework that greatly accelerates gains in prediction performance. Key advances include the discovery of sequence motifs and improved quantification of the roles of gene expression and proteasomal processing.