Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.

译文

主要组织相容性复合体II (MHC II) 分子在细胞免疫的发作和控制中起着至关重要的作用。在高度选择性的过程中,MHC II在抗原呈递细胞表面呈递源自外源抗原的肽,用于T细胞检查。了解定义此演示文稿的规则具有对细胞免疫系统的调节和潜在操纵的重要见解。在这里,我们应用NNAlign_MA机器学习框架来分析和整合大规模洗脱的MHC II配体质谱 (MS) 数据集,以推进CD4表位的预测。NNAlign_MA允许混合数据类型的集成,处理具有多个潜在等位基因注释的配体,对配体上下文进行编码,利用数据集之间的信息,并且具有泛特异性的能力,允许在训练数据中包含的分子集之外进行准确的预测。应用此框架,我们确定了MS data描述的50多个MHC II类分子的准确结合基序,尤其是将DP和DQ的覆盖范围扩大到使用当前MS motif反卷积技术获得的范围之外。此外,在大规模基准测试中,被称为NetMHCIIpan-4.0的最终模型展示了超越当前配体和CD4 + T细胞表位预测的最新预测指标的改进性能。这些结果表明,NNAlign_MA和NetMHCIIpan-4.0是分析免疫肽MS数据,预测T细胞表位和开发个性化免疫疗法的强大工具。

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