Department of Biochemistry, University of Washington, Seattle, Washington, USA.
J Proteome Res. 2010 Oct 1;9(10):5438-44. doi: 10.1021/pr1006685.
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/.
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肽中来诱导肽骨架的断裂。与碰撞诱导解离不同，侧链和修饰如磷酸化在 ETD 过程中保持完整。由于前体荷质比是 MS/MS 序列数据库搜索工具的重要输入，因此准确确定前体荷质比有助于鉴定过程。此外，由于 ETD 可应用于大的、高电荷的肽，因此需要准确确定前体荷质比。否则，必须使用大量可能的前体荷质比重复搜索每个光谱。为了解决这个问题，我们开发了一种基于支持向量机分类器的 ETD 荷质比预测工具，该工具具有出色的分类准确性，同时最小化了预测的荷质比的总数。该工具是免费的、开源的、跨平台兼容的，并且与现有的荷质比预测工具相比表现良好。该程序可从 http://code.google.com/p/etdz/ 获得。
