Development of a rapid glycosylation profiling system using mass spec data

该提案的最终目标是开发一种全自动的分析方法 并将该方法应用于分析几种不同的HIV-Env 糖蛋白这项工作将通过完成三个具体目标来完成：目标1： 使用MS数据作为输入，开发快速识别糖肽的算法 用于单一糖基化糖肽的组合物。目标2：开发一种算法， 表征多重糖基化糖肽。目标3：使用目标1和2中的工具， 筛选HIV Env蛋白上可接近和遗传保守的点。大部分 目标1和目标2的工作将通过协同地将大量的专门知识纳入 光谱（MS）数据采集和分析，具有软件设计/开发方面的专业知识。这 这项工作可以广泛影响人类健康研究，因为糖肽分析是一种使 可用于筛选疾病或疾病状态的生物标志物的生物分析技术; 它可用于帮助验证基于糖蛋白的药物的安全性和一致性; 它可用于分析HIV包膜蛋白（HIV的主要靶标）上的糖肽 疫苗研发。在完成自动化糖基化分析系统（目的1）之后， 2）该系统可用于鉴定各种HIV-1病毒上的糖肽成分， 与巴顿F.海恩斯在杜克大学 医学中心该方法的方法包括制备每一种胰蛋白酶的胰蛋白酶解物。 糖蛋白，对消化产物进行HPLC-MS和LC-MS/MS分析，以及 使用开发的糖肽分析软件解释结果。完成后 这项工作，Env蛋白的糖肽谱将提供有关表位的信息， 可访问性环境。这与人类健康有关，因为鉴定表位在 在HIV-1进化枝中一致暴露的蛋白质是设计HIV-1的第一步。 模拟这些表位并产生广泛中和HIV-1抗体的疫苗。

The ultimate goal of this proposal is to develop a fully-automated approach for analysis of glycopeptides and to apply that approach to the analysis of several different HIV-Env glycoproteins. This work would be accomplished by completing three specific aims: AIM 1: Using MS data as the input, develop an algorithm that rapidly identifies glycopeptide compositions for singly glycosylated glycopeptides. AIM 2: Develop an algorithm that characterizes multiply glycosylated glycopeptides. AIM 3: Use the tools in AIMS 1 and 2 to screen for accessible and genetically conserved points on the HIV Env protein. The bulk of the work, Aims 1 and 2, would be completed by synergistically incorporating expertise in mass spectral (MS) data acquisition and analysis with expertise in software design/development. This work can broadly impact human health research because glycopeptide analysis is an enabling bioanalytical technology that can be used to screen for biomarkers of disease or disease state; it can be used to help verify the safety and consistency of glycoprotein-based pharmaceuticals; and it can be used to analyze glycopeptides on HIV Envelope proteins, a major target for HIV vaccine development. After completion of the automated glycosylation profiling system (Aims 1 and 2) this system would be used to identify the glycopeptide composition on a variety of HIV- Env vaccine candidates generated in collaboration with Dr. Barton F. Haynes at Duke University Medical Center. The method for this approach includes preparing tryptic digests of each of the glycoproteins, conducing HPLC-MS and LC-MS/MS analyses on the digested products, and interpreting the results using the developed glycopeptide analysis software. After completion of this work, the glycopeptide profiles of the Env proteins would provide information about epitope accessibility on Env. This is relevant to human health because identifying epitopes on the protein that are consistently exposed across HIV-1 clades is a first step in designing an HIV vaccine that mimics these epitopes and elicits broadly neutralizing antibodies to HIV-1.