HLA Class II specificity assessed by high-density peptide microarray interactions

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 (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate 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 is 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. Here, we have used recombinant HLA-DRB1*01...预测并鉴定主要组织相容性复合体（Major Histocompatibility Complex, MHC）结合肽的能力，是T细胞表位发现的核心环节；该技术最终将助力疫苗与免疫治疗的研发进程。具体而言，MHC I类（MHC class I, MHC-I）预测工具已发展至成熟阶段，几乎可针对所有MHC-I同种异型精准筛选最优肽表位；相较之下，当前的MHC II类（MHC class II, MHC-II）预测工具仍有待完善。由于受MHC-II限制性的CD4+ T细胞调控并协调绝大多数免疫应答，这一短板严重制约了高效免疫治疗的开发。获取大规模肽库及后续海量肽-MHC-II相互作用数据的能力，正是解决该问题的关键；该解决方案同时也将推动生物信息学预测工具的性能优化——此类工具的开发高度依赖大规模精准、多样且具有代表性的数据集。本研究中，我们采用了重组人类白细胞抗原（Human Leukocyte Antigen, HLA）-DRB1*01……