IMPROVING PROTEIN IDENTIFICATION SENSITIVITY BY COMBINING MS AND MS/MS

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。 我们研究和比较了三种鸟枪蛋白鉴定方法，结合MS和MS/MS信息，使用LTQ-Orbitrap高质量精度数据。在第一种方法中，我们采用了唯一质量标识符方法，其中首先减去与从MS/MS数据库搜索鉴定的蛋白质预测的肽相匹配的MS峰，然后使用MS峰作为蛋白质鉴定的唯一质量标识符。在第二种方法中，我们使用了一种精确的质量和时间标签方法，通过从以前的MudPIT分析中建立一个潜在的质量和保留时间数据库。对于第三种方法，我们使用了肽质量指纹的方法结合随机数据库的蛋白质鉴定。我们表明，我们可以提高低丰度蛋白质的蛋白质鉴定灵敏度相结合的MS和MS/MS信息。此外，从MS/MS数据库搜索的“一击奇迹”可以进一步证实MS信息和方法提高低丰度蛋白质的鉴定。然后讨论了这三种方法的优点和缺点。

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. We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudPIT analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, "one-hit wonders" from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.