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的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 交联肽的质谱鉴定可以提供有关蛋白质复合物结构的有价值的信息。我们描述了一个简单的数据库搜索方案，识别和分配统计置信度估计光谱交联肽。该方法非常适合于单一蛋白质复合物的靶向分析，而不需要同位素标记策略。我们的方法使用SEQUEST风格的搜索程序，其中数据库由具有和不具有连接子的单肽和交联产物的混合物组成。与以前的几种方法相比，我们产生的理论光谱，占所有的预期峰从交联产品，我们采用经验曲线拟合程序来估计统计置信度。我们表明，我们的全自动程序成功地重新识别光谱从以前的研究，我们提供的证据表明，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.