PRECURSOR CHARGE STATE PREDICTION FOR ELECTRON TRANSFER DISSOCIATION TANDEM MASS

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级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的多肽而导致沿着多肽骨架的碎裂。与碰撞诱导的解离相反，侧链和修饰(如磷酸化)在ETD过程中保持不变。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识别过程。此外，由于ETD可以应用于大的、高电荷的多肽，因此对准确的前体电荷状态确定的需求被放大。否则，必须使用大范围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，我们开发了一个基于支持向量机分类器的ETD电荷状态预测工具，该工具被证明在最小化预测电荷状态的总数量的同时，具有更高的分类精度。该工具免费可用，开源，跨平台兼容，与现有的电荷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

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/.