Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.
Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.
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DOI：
10.1371/journal.pcbi.1005725
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发表时间：
2017-08
影响因子：
4.3
通讯作者：
Gfeller D
中科院分区：
文献类型：
--
作者：
Bassani-Sternberg M;Chong C;Guillaume P;Solleder M;Pak H;Gannon PO;Kandalaft LE;Coukos G;Gfeller D
人类白细胞抗原 I 类 (HLA-I) 结合基序的精确鉴定对于我们理解和预测传染病和癌症中（新）抗原呈现的能力起着核心作用。在这里，通过利用十个新生成的以及四十个公共 HLA 肽组学数据集（包含超过 115,000 个独特肽）中 HLA-I 等位基因的共现，我们表明，我们可以快速准确地识别许多 HLA-I 结合基序，并将它们映射到相应的等位基因，而无需任何 HLA-I 结合特异性的先验知识。我们的方法概括并完善了 43 个最常见等位基因的已知基序，发现了 9 个等位基因的新基序（迄今为止，这些等位基因的已知配体少于 5 个），并提供了一个可扩展的框架，以便在未来纳入更多的 HLA 肽组学研究。精炼的基序改善了新抗原和癌症睾丸抗原的预测，表明无偏见的 HLA 肽组学数据非常适合根据肿瘤外显子组测序数据对新抗原进行计算机预测。新的基序进一步揭示了 HLA-I 结合位点但在 B 袋之外的残基对某些 HLA-I 等位基因 P2 处的结合特异性的远程调节，我们通过蛋白质结构分析、诱变和体外结合测定揭示了潜在的机制。预测免疫系统可见的癌症细胞和正常细胞之间的差异对于癌症免疫治疗至关重要。在这里，我们介绍了一种新颖的计算框架，以利用来自深入的 HLA 肽组学研究的大量数据，包括为此工作生成的 10 个新颖的高质量 (<1% FDR) 数据集，以改进对 HLA-I 分子上显示的肽的预测。这些高通量和公正的数据使我们能够改进许多等位基因（包括一些在本研究之前没有配体的等位基因）的 HLA-I 结合特异性模型，并改进根据黑色素瘤和肺癌样本的外显子组测序数据对新抗原的预测。此外，对 HLA-I 结合特异性的精细描述揭示了 HLA-I 结合特异性在其配体的第二个氨基酸位置 (P2) 上通过属于 HLA-I 结合位点但在 B 口袋之外的残基进行变构调节的情况。
The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays. Predicting the differences between cancer and normal cells that are visible to the immune system is of central importance for cancer immunotherapy. Here we introduce a novel computational framework to harness the wealth of data from in-depth HLA peptidomics studies, including ten novel high quality (<1% FDR) datasets generated for this work, to improve predictions of peptides displayed on HLA-I molecules. These high-throughput and unbiased data enable us to refine models of HLA-I binding specificity for many alleles (including some that had no ligand until this study) and improve predictions of neo-antigens from exome sequencing data in melanoma and lung cancer samples. Moreover, the refined description of HLA-I binding specificity reveals cases of allosteric modulation of HLA-I binding specificity at the second amino acid position (P2) of their ligands by residues that are part of the HLA-I binding site but outside of the B pocket.
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影响因子：
9.9
作者：
通讯作者：
--
影响因子：
32.4
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
通讯作者：
Wu CJ
影响因子：
14.9
作者：
Almeida LG;Sakabe NJ;deOliveira AR;Silva MC;Mundstein AS;Cohen T;Chen YT;Chua R;Gurung S;Gnjatic S;Jungbluth AA;Caballero OL;Bairoch A;Kiesler E;White SL;Simpson AJ;Old LJ;Camargo AA;Vasconcelos AT
通讯作者：
Vasconcelos AT
影响因子：
4.4
作者：
Bassani-Sternberet, Michal;Gfellert, David
通讯作者：
Gfellert, David
影响因子：
--
作者：
HILDEBRAND, WH;DOMENA, JD;PARHAM, P
通讯作者：
PARHAM, P