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

 描述(由申请人提供)：基于抗原结构的监督学习用于预测CD4T细胞表位作为适应性免疫反应的一部分，CD4T细胞提供多种保护功能，包括细胞因子介导的和接触介导的B细胞、CD8T细胞和先天免疫细胞的信号，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方面最关键的作用尤其鲜为人知。研究和疫苗设计的一个主要障碍是缺乏用于计数和跟踪CD4T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来预测CD4T细胞表位，这将使免疫学和疫苗学中最有前途但目前受阻的领域之一取得进展。CD4T细胞表位优势比CD8T细胞表位优势更难预测，这是因为II类MHC抗原提呈蛋白的选择性较低，而且蛋白水解性抗原处理对多肽配体的可用性有很大影响。在胞内室中，蛋白酶作用于大多数天然折叠的抗原，其3D结构将蛋白分解引导到灵活无序的片段。因此，柔性片段中潜在的MHC结合序列被破坏，稳定抗原片段中的序列被优先加载并呈递给CD4T细胞。我们将把这种对稳定抗原片段中表位优势的偏向纳入到一个计算工具中，该工具将显著改进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位置特定评分矩阵方法。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，我们将使用它来预测来自鼠伤寒沙门氏菌和假鼻疽伯克霍尔德氏菌的5种可溶性分泌抗原的新表位，这两种生物对CD4T细胞免疫是必不可少的。在接触完整抗原的两种类型--细菌感染和亚单位疫苗接种后，将在单个C57BL/6小鼠身上测试与预测表位的第80%百分位相对应的多肽的反应。

 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.