PRECURSOR CHARGE STATE PREDICTION FOR ELECTRON TRANSFER DISSOCIATION TANDEM MASS

电子转移解离串联质量的前体电荷态预测

• 批准号: 8365872
• 负责人: William Noble
• 金额: $ 5.42万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8365872
• 关键词: Address; Anions; Biology; Charge; Classification; Code; Databases; Dissociation; Electron Transport; Exhibits; Funding; Fungal Genome; Grant; Left; Machine Learning; Modification; National Center for Research Resources; Peptides; Phosphorylation; Principal Investigator; Process; Research; Research Infrastructure; Resources; Side; Source; TANDEM; United States National Institutes of Health; Vertebral column; base; cost; open source; programs; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Electron-transfer dissociation (ETD) induces fragmentation along the peptide backbone by transferring an electron from a radical anion to a protonated peptide. In contrast with collision-induced dissociation, side chains and modifications such as phosphorylation are left intact through the ETD process. Because the precursor charge state is an important input to MS/MS sequence database search tools, the ability to accurately determine the precursor charge is helpful for the identification process. Furthermore, because ETD can be applied to large, highly charged peptides, the need for accurate precursor charge state determination is magnified. Otherwise, each spectrum must be searched repeatedly using a large range of possible precursor charge states. To address this problem, we have developed an ETD charge state prediction tool based on support vector machine classifiers that is demonstrated to exhibit superior classification accuracy while minimizing the overall number of predicted charge states. The tool is freely available, open source, cross platform compatible, and demonstrated to perform well when compared with an existing charge state prediction tool. The program is available from http://code.google.com/p/etdz/.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 电子转移解离（ETD）通过将电子从自由基阴离子转移到质子化肽来诱导沿着肽骨架的片段化。与碰撞诱导解离相反，侧链和修饰（如磷酸化）通过ETD过程保持完整。由于前体电荷状态是MS/MS序列数据库搜索工具的重要输入，因此准确确定前体电荷的能力有助于鉴定过程。此外，由于ETD可以应用于大的、高电荷的肽，因此放大了对准确的前体电荷状态确定的需求。否则，必须使用大范围的可能的前体电荷状态重复搜索每个光谱。为了解决这个问题，我们已经开发了一种基于支持向量机分类器的ETD充电状态预测工具，该工具被证明具有上级分类精度，同时最小化预测的充电状态的总数。该工具是免费提供的，开源的，跨平台兼容，并证明与现有的充电状态预测工具相比，表现良好。该程序可从http://code.google.com/p/etdz/获得。