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HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions
The Journal of Immunology ( IF 4.4 ) Pub Date : 2020-06-01 , DOI: 10.4049/jimmunol.2000224
Thomas Osterbye 1 , Morten Nielsen 2, 3 , Nadine L Dudek 4 , Sri H Ramarathinam 4 , Anthony W Purcell 4 , Claus Schafer-Nielsen 5 , Soren Buus 6
Affiliation  

Key Points MHC class II specificity was assessed by high-density peptide arrays. A novel method is proposed to expand and update MHC class II prediction algorithms. The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4+ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide–MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide–MHC-II binding data as well as improving MHC-II prediction tools.

中文翻译:

通过高密度肽微阵列相互作用评估的 HLA II 类特异性

关键点 MHC II 类特异性通过高密度肽阵列进行评估。提出了一种扩展和更新MHC II类预测算法的新方法。预测和/或识别 MHC 结合肽的能力是 T 细胞表位发现的重要组成部分,最终应该有利于疫苗和免疫疗法的开发。特别是,MHC I 类预测工具已经成熟到可以为几乎所有 MHC I 类同种异型准确选择最佳肽表位的程度;相比之下,目前的 MHC II 类 (MHC-II) 预测因子不太成熟。由于 MHC-II 限制了 CD4+ T 细胞控制和协调大多数免疫反应,这一缺点严重阻碍了有效免疫疗法的发展。生成大量肽以及随后生成大量肽-MHC-II 相互作用数据的能力是解决这个问题的关键,该解决方案也将支持生物信息学预测因子的改进,这严重依赖于大量的可用性准确、多样和有代表性的数据。在这项研究中,我们使用了 rHLA-DRB1*01:01 和 HLA-DRB1*03:01 分子来询问高密度肽阵列,其中包含一式三份的 70,000 个随机肽。我们证明获得的结合数据包含反映所研究的 HLA-DR 分子特异性的系统和可解释信息,适合训练预测因子,能够预测从人类 EBV 转化的 B 细胞中洗脱的 T 细胞表位和肽。总的来说,
更新日期:2020-06-01
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