DETECTING CROSS-LINKED PEPTIDES BY SEARCHING AGAINST A DATABASE OF CROSS-LINKED

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 交联肽的质谱学鉴定可以提供有关蛋白质复合体结构的有价值的信息。我们描述了一种简单的数据库搜索方案，该方案识别并分配来自交联肽的光谱的统计置信度估计。该方法非常适合单个蛋白质复合体的靶向分析，不需要同位素标记策略。我们的方法使用SEQUEST式的搜索程序，其中数据库由带有和不带有连接物和交联物的单肽的混合物组成。与以前的几种方法不同，我们生成的理论光谱可以解释交联物的所有预期峰值，并且我们使用经验曲线拟合程序来估计统计置信度。我们表明，我们的全自动化程序成功地重新识别了先前研究中的光谱，并提供了证据，证明我们的统计置信度估计是准确的。

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. Mass spectrometric identification of cross-linked peptides can provide valuable information about the structure of protein complexes. We describe a straightforward database search scheme that identifies and assigns statistical confidence estimates to spectra from cross-linked peptides. The method is well suited to targeted analysis of a single protein complex, without requiring an isotope labeling strategy. Our approach uses a SEQUEST-style search procedure in which the database is comprised of a mixture of single peptides with and without linkers attached and cross-linked products. In contrast to several previous approaches, we generate theoretical spectra that account for all of the expected peaks from a cross-linked product, and we employ an empirical curve-fitting procedure to estimate statistical confidence measures. We show that our fully automated procedure successfully reidentifies spectra from a previous study, and we provide evidence that our statistical confidence estimates are accurate.