Comprehensive Glycoproteomic Tool Development for Cancer Biomarkers

描述（由申请人提供）：该项目解决了对更好的方法的需求，用于破译临床样品中蛋白质的糖基化。糖基化是蛋白质结构和功能的重要修饰剂，并有助于疾病过程。但我们目前对大多数蛋白质的糖基化知之甚少。目前用于探测蛋白质上聚糖的方法不适合满足这一需求，因为它们需要大量材料和许多加工步骤。在这里，我们提出了一个实用的方法来探测蛋白质糖基化，将提供：1）能够获得结构和组成信息与有限的样品 用途; 2）能够精确比较样本之间的聚糖水平;和3）随时转化为临床测定。我们将实现这一目标，通过新的信息技术，促进结合使用质谱（MS）和凝集素结合研究聚糖。第二阶段将集中于胰腺癌的糖蛋白生物标志物。MS提供了聚糖的单糖组成和一些序列信息，但它留下了关于序列或连接变体的模糊性。同样，凝集素可以使用少量样品精确测量特定结构，但它们不能提供每个聚糖的完整图像。我们预测，定量整合这两种类型的信息将得到更准确的信息比单独的方法。我们将定量连接凝集素实验MS实验使用的共同语言的基序-聚糖的亚结构。在目标1中，我们将开发一种算法，用于基于凝集素结合来识别样品中最可能存在的聚糖基序。在目标2中，我们将开发集成工具， 凝集素和MS数据，并将使用该方法来表征和比较三种不同的纯化糖蛋白的聚糖。我们将确定MS和凝集素数据的链接是否比单独使用任何一种方法提供更完整的信息，有限的样品消耗和样品之间进行精确比较的能力。

DESCRIPTION (provided by applicant): This project addresses the need for better methods for deciphering the glycosylation of proteins in clinical samples. Glycosylation is an important modifier of protein structure and function and contributes to disease processes. But we currently know little about the glycosylation of most proteins. The current methods for probing glycans on proteins are not suitable for meeting this need, as they require much material and many processing steps. Here we propose and practical approach to probing protein glycosylation that will provide: 1) the ability to obtain structural and compositional information with limited sample usage; 2) the ability to precisely compare glycan levels between samples; and 3) ready translation into a clinical assay. We will achieve this goal through novel informatics techniques that facilitate the combined use of mass spectrometry (MS) and lectin binding for studying glycans. Phase II will focus on glycoprotein biomarkers of pancreatic cancer. MS provides the monosaccharide compositions of glycans and some sequence information, but it leaves ambiguities about sequence or linkage variants. Likewise, lectins can give precise measurements of specific structures using small amounts of sample, but they do not provide a complete picture of each glycan. We predict that quantitatively integrating the two types of information will give more accurate information than either method alone. We will quantitatively link lectin experiments to MS experiments using the common language of motifs - substructures of glycans. In Aim 1, we will develop an algorithm for identifying what glycan motifs are most likely present in a sample based on lectin binding. In Aim 2, we will develop tools for integrating lectin and MS data and will use the method to characterize and compare the glycans of three different purified glycoproteins. We will determine whether the linking of MS and lectin data provides more complete information than either method alone, with limited sample consumption and the ability to make precise comparisons between samples.