Koch Institute for Integrative Cancer Research, Cambridge, MA, USA.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Commun. 2020 Sep 4;11(1):4414. doi: 10.1038/s41467-020-18204-2.
CD4 helper T cells contribute important functions to the immune response during pathogen infection and tumor formation by recognizing antigenic peptides presented by class II major histocompatibility complexes (MHC-II). While many computational algorithms for predicting peptide binding to MHC-II proteins have been reported, their performance varies greatly. Here we present a yeast-display-based platform that allows the identification of over an order of magnitude more unique MHC-II binders than comparable approaches. These peptides contain previously identified motifs, but also reveal new motifs that are validated by in vitro binding assays. Training of prediction algorithms with yeast-display library data improves the prediction of peptide-binding affinity and the identification of pathogen-associated and tumor-associated peptides. In summary, our yeast-display-based platform yields high-quality MHC-II-binding peptide datasets that can be used to improve the accuracy of MHC-II binding prediction algorithms, and potentially enhance our understanding of CD4 T cell recognition.
CD4 辅助 T 细胞通过识别由 II 类主要组织相容性复合物 (MHC-II) 呈递的抗原肽，为病原体感染和肿瘤形成期间的免疫反应做出重要贡献。虽然已经报道了许多用于预测肽与 MHC-II 蛋白结合的计算算法，但它们的性能差异很大。在这里，我们提出了一种基于酵母展示的平台，该平台允许鉴定比可比方法多一个数量级的独特 MHC-II 结合物。这些肽包含先前鉴定的基序，但也揭示了新的基序，这些基序通过体外结合测定得到验证。使用酵母展示文库数据训练预测算法可提高肽结合亲和力的预测和病原体相关肽和肿瘤相关肽的鉴定。总之，我们基于酵母展示的平台产生了高质量的 MHC-II 结合肽数据集，可用于提高 MHC-II 结合预测算法的准确性，并有可能增强我们对 CD4 T 细胞识别的理解。
