AUTOMATED ID OF PROTEINS, VARIANTS & POST-TRANSLATIONAL MODIFICATIONS

蛋白质、变体的自动识别

• 批准号: 7369203
• 负责人: DAVID H K CHUI
• 金额: $ 0.17万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7369203
• 关键词: AUTOMATED; ID; PROTEINS; VARIANTS; POST

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. More than 1200 recognized hemoglobin (Hb) structural variants exist in the human population, many of which may clinically manifest themselves as a disease state. Additionally, post-translational modifications (PTMs) of Hb that are not observed in gene-based analysis may play an important role in this disease. A reliable LC-MS/MS technique coupled with a proteomics based data analysis approach is needed to fully identify HB variants and their PTMs. In this study, a robust and reliable LC-MS/MS proteomics based method for automated characterization of Hb protein variants and PTMs is being explored. Whole blood is washed with PBS, diluted and frozen prior to use. Proteins are separated and digested with trypsin and the digests are analyzed by online LC-MS/MS (QTOF-API-US, Waters Corporation). Accurate mass is calculated in real time by operation of a reference Lockspray. Data are processed using ProteinLynx Global Server 2.05 (Waters) and searched against SwissProt and custom programmed Hemoglobin/PTM databases. In our method, as minimal purification, derivatization or separation is required, the sample preparation is considerably simplified. Full automation of data analysis methodology has been established by which the biases and skill of an operator do not limit the success of the identification process. Data analysis time is reduced by using a pre-programmed Hb database search in which Hb-derived tryptic peptides (including variants and modifications) are unambiguously identified, while the peptides not derived from Hb can be digitally filtered out, based on the accuracies of mass measurement of the high resolution QTOF instrument. Various PTMs have also been programmed for automatic PTM search. This simplified sample preparation procedure and the automated data analysis are critical components of this proteomic approach in enabling high through-put sample analysis/data interpretation and ensure precise and accurate data consistency. Samples of Hb that cannot be analyzed by the standard CE-based protocols of the BUSM Sickle Cell laboratory are now frequently analyzed by LC/MS and MS/MS. LC-MS/MS allows the detection of variants, provides a rapid alternative to DNA analysis with additional information obtained for PTMs and has potential for clinical use. (A separately-described Core Research project is exploring the development of even more advanced approaches to this analytical challenge.)

该子项目是利用 NIH/NCRR 资助的中心拨款提供的资源的众多研究子项目之一。子项目和研究者 (PI) 可能已从另一个 NIH 来源获得主要资金，因此可以在其他 CRISP 条目中得到体现。列出的机构是中心的机构，不一定是研究者的机构。人类中存在超过 1200 种公认的血红蛋白 (Hb) 结构变异，其中许多变异可能在临床上表现为一种疾病状态。此外，在基于基因的分析中未观察到的 Hb 翻译后修饰 (PTM) 可能在这种疾病中发挥重要作用。需要可靠的 LC-MS/MS 技术与基于蛋白质组学的数据分析方法相结合，以充分识别 HB 变异及其 PTM。在本研究中，正在探索一种基于 LC-MS/MS 蛋白质组学的稳健可靠的方法，用于 Hb 蛋白变异体和 PTM 的自动表征。全血在使用前用 PBS 洗涤、稀释并冷冻。分离蛋白质并用胰蛋白酶消化，并通过在线 LC-MS/MS（QTOF-API-US，沃特世公司）分析消化物。通过操作参考 Lockspray 实时计算精确质量。使用 ProteinLynx Global Server 2.05 (Waters) 处理数据，并根据 SwissProt 和定制编程的血红蛋白/PTM 数据库进行搜索。在我们的方法中，由于需要最少的纯化、衍生化或分离，因此样品制备大大简化。已经建立了完全自动化的数据分析方法，操作员的偏见和技能不会限制识别过程的成功。通过使用预编程的 Hb 数据库搜索来缩短数据分析时间，其中明确识别 Hb 衍生的胰蛋白酶肽（包括变体和修饰），同时基于高分辨率 QTOF 仪器的质量测量精度，可以以数字方式过滤掉非 Hb 衍生的肽。各种 PTM 也已编程用于自动 PTM 搜索。这种简化的样品制备程序和自动化数据分析是这种蛋白质组学方法的关键组成部分，可实现高通量样品分析/数据解释并确保精确和准确的数据一致性。无法通过 BUSM 镰状细胞实验室基于 CE 的标准方案进行分析的 Hb 样品现在经常通过 LC/MS 和 MS/MS 进行分析。 LC-MS/MS 可以检测变异，提供 DNA 分析的快速替代方案，并获得 PTM 的附加信息，并且具有临床应用的潜力。 （一个单独描述的核心研究项目正在探索开发更先进的方法来应对这一分析挑战。）