DEVELOPMENT OF PTMAP20 FOR GENOME-WIDE PROTEIN POSTTRANSLATIONAL MODIFICATION

用于全基因组蛋白质翻译后修饰的 PTMAP20 的开发

• 批准号: 7956366
• 负责人: YINGMING ZHAO
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956366
• 关键词: Biomedical Research; Cells; Chemicals; Computer Retrieval of Information on Scientific Projects Database; Computers; Data; Development; Funding; Goals; Grant; High Performance Computing; Institution; Mass Spectrum Analysis; Modification; Post-Translational Protein Processing; Proteins; Research; Research Personnel; Resources; Software Tools; Source; System; United States National Institutes of Health; abstracting; genome-wide; parallel computing; protein function; tool

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. No Abstract AvailablePosttranslational modifications (PTMs) are essential for protein functions in cell. Mass spectrometry is capable to identify PTMs with high sensitivity comparing to traditional chemical approach. We have developed a new software tool, called PTMap, that is capable to identify all-types of PTMs in a single protein from mass spectrometry data. Here, we propose to combine PTMap with the powerful computer clusters from Teragrid system and extend PTM analysis by PTMap through large-scale parallel computation. Our goal is to develop PTMap2.0 tool for a genome-wide characterization of protein posttranslational modifications.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 无可用摘要 翻译后修饰 (PTM) 对于细胞中的蛋白质功能至关重要。与传统化学方法相比，质谱法能够以高灵敏度识别 PTM。我们开发了一种名为 PTMap 的新软件工具，能够从质谱数据中识别单个蛋白质中所有类型的 PTM。在这里，我们建议将 PTMap 与 Teragrid 系统强大的计算机集群相结合，并通过大规模并行计算扩展 PTMap 的 PTM 分析。我们的目标是开发 PTMap2.0 工具，用于蛋白质翻译后修饰的全基因组表征。