Development of a rapid glycosylation profiling system using mass spec data

这项提议的最终目标是开发一种完全自动化的分析方法 并将该方法应用于几种不同的HIV环境的分析 糖蛋白。这项工作将通过完成三个具体目标来完成：目标1： 利用MS数据作为输入，开发一种快速识别糖肽的算法 单糖化糖肽的组合物。目标2：开发一种算法 表征了多个糖基化的糖肽。目标3：使用目标1和目标2中的工具 筛选HIV env蛋白上可接近的和遗传上保守的点。世界上的大部分 目标1和目标2的工作将通过协同结合大量的专业知识来完成 具有软件设计/开发专业知识的光谱(MS)数据采集和分析。这 工作可以广泛地影响人类健康研究，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； 它可以用来帮助验证以糖蛋白为基础的药物的安全性和一致性； 它可以用于分析HIV包膜蛋白上的糖肽，HIV包膜蛋白是HIV的主要靶点 疫苗研发。在完成自动糖基化分析系统(AIMS 1 2)该系统将用于鉴定多种HIV病毒上的糖肽组成。 与杜克大学巴顿·F·海恩斯博士合作开发的环境疫苗候选 医疗中心。这种方法的方法包括准备每一种 糖蛋白，对消化产物进行LC-MS和LC-MS/MS分析，以及 用开发的糖肽分析软件对结果进行解释。在完成后 这项工作，环境蛋白的糖肽图谱将提供关于表位的信息 环境上的可访问性这与人类健康有关，因为识别表位 在HIV-1分支中持续暴露的蛋白质是设计HIV的第一步 模拟这些表位并引起广泛中和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.