Antigen Structure-Based Supervised Learning for CD4+ T-cell Epitope Prediction

 描述（由申请人提供）：用于CD 4 + T细胞表位预测的基于抗原结构的监督学习 作为适应性免疫应答的一部分，CD 4 + T细胞提供许多保护性功能，包括对B细胞、CD 8 + T细胞和先天免疫细胞的丝氨酸介导的和接触介导的信号，以及对病原体的直接攻击模式。然而，它们在防御细胞内细菌病原体中的最关键作用尤其知之甚少。研究和疫苗设计的主要障碍是缺乏用于计数和追踪CD 4 + T细胞的表位特异性试剂。我们建议开发一种新的和经过充分验证的算法，用于CD 4 + T细胞表位预测，这将使最有前途的，但目前阻碍免疫学和疫苗学领域之一的进展。CD 4 + T细胞表位优势比CD 8 + T细胞表位优势更不可预测，因为II类MHC抗原呈递蛋白的选择性较低，并且因为蛋白水解抗原加工对肽配体的可用性具有主要影响。在内吞区室中，蛋白酶主要作用于天然折叠的抗原，其3D结构将蛋白水解引导至柔性无序区段。因此，柔性区段中的潜在MHC结合序列被破坏，并且稳定抗原区段中的序列优先加载并呈递给CD 4 + T细胞。我们将把这种偏向表位优势的稳定抗原片段到一个计算工具，显着改善现有的基于序列的表位预测方法。我们将使用几种可能的监督学习技术来开发基于稳定性的方法，包括隐马尔可夫模型和特定位置的评分矩阵方法。对于可溶性抗原，我们的特征集将是构象稳定性数据，包括晶体学b因子、表面可及性、COREX残基稳定性和序列熵。为了验证我们的方法，我们将用它来预测新的抗原表位5可溶性分泌抗原从鼠伤寒沙门氏菌和类鼻疽伯克霍尔德氏菌，生物体的CD 4 + T细胞免疫是必不可少的。在两种类型的暴露于完整抗原（细菌感染和亚单位疫苗接种）后，将测试对应于预测表位的第80百分位的肽在个体C57 BL/6小鼠中的应答。

 DESCRIPTION (provided by applicant): Antigen Structure-Based Supervised Learning for CD4+ T-cell Epitope Prediction CD4+ T cells provide numerous protective functions as part of the adaptive immune response, including cytokine-mediated and contact-mediated signals to B cells, CD8+ T cells, and innate- immune cells, as well as direct modes of attack on pathogenic agents. Nevertheless, their most critical roles in defense against intracellular bacterial pathogens are especially poorly understood. A major barrier to both study and vaccine design has been the lack of epitope- specific reagents for counting and tracking CD4+ T cells. We propose to develop a novel and well-validated algorithm for CD4+ T-cell epitope prediction that will enable progress in one of the most promising, yet presently hindered fields of immunology and vaccinology. CD4+ T-cell epitope dominance has been much less predictable than CD8+ T-cell epitope dominance because the class II MHC antigen-presenting protein is less selective and because proteolytic antigen processing has a major influence on the availability of peptide ligands. In the endocytic compartments, proteases act on mostly natively folded antigens, whose 3D structure directs proteolysis to the flexibly disordered segments. Thus, potential MHC-binding sequences in the flexible segments are destroyed, and sequences in the stable antigen segments are preferentially loaded and presented to CD4+ T cells. We will incorporate this bias toward epitope dominance in the stable antigen segments into a computational tool that significantly improves upon existing sequence-based methods for epitope prediction. We will develop our stability-based method using several possible supervised learning techniques, including hidden Markov models and position-specific scoring matrix methods. For soluble antigens, our feature set will be conformational stability data including crystallographic b-factor, surface-accessibility, COREX residue stabilities, and sequence entropy. In order to validate our method, we will use it to predict novel epitopes for 5 soluble secreted antigens from Salmonella typhimurium and Burkholderia pseudomallei, organisms for which CD4+ T-cell immunity is essential. Peptides corresponding to the 80th- percentile of predicted-epitopes will be tested for responses in individual C57BL/6 mice, following two types of exposure to the intact antigens, bacterial infection and subunit vaccination.