OPTIMIZING SEARCH CONDITIONS FOR THE MASS FINGERPRINT-BASED ID OF PROTEINS

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。 通过使用MS数据搜索蛋白质序列集合进行蛋白质鉴定的两个中心问题是最佳使用实验信息以允许鉴定低丰度蛋白质和准确分配结果为假的概率。对于基于MS的蛋白质综合识别，需要选择合适的算法和最优搜索条件。我们使用概率算法对不同序列集合搜索条件下基于PMF的蛋白质识别的质量进行了系统的研究，该算法为每个结果赋予了统计意义。我们使用了2244个来自2-DE分离的人血浆蛋白的PMF，并在不同的搜索约束下进行了鉴定：质量精度(0.01-0.3 Da)、最大缺失切割位点数(0-2)和搜索的序列集合的大小(5.6×10(4)-1.8×10(5))。通过统计每个条件的显著结果(显著水平0.05、0.01和0.001)的数量，我们证明了搜索条件对蛋白质组分析实验的成功结果的影响。为了提高鉴定的统计学意义，测试了一种利用白蛋白匹配肽的质量偏差的质量校正程序，并使用迭代搜索从每个PMF中鉴定多个蛋白质。

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. The two central problems in protein identification by searching a protein sequence collection with MS data are the optimal use of experimental information to allow for identification of low abundance proteins and the accurate assignment of the probability that a result is false. For comprehensive MS-based protein identification, it is necessary to choose an appropriate algorithm and optimal search conditions. We report a systematic study of the quality of PMF-based protein identifications under different sequence collection search conditions using the Probability algorithm, which assigns the statistical significance to each result. We employed 2244 PMFs from 2-DE-separated human blood plasma proteins, and performed identification under various search constraints: mass accuracy (0.01-0.3 Da), maximum number of missed cleavage sites (0-2), and size of the sequence collection searched (5.6 x 10(4)-1.8 x 10(5)). By counting the number of significant results (significance levels 0.05, 0.01, and 0.001) for each condition, we demonstrate the search condition impact on the successful outcome of proteome analysis experiments. A mass correction procedure utilizing mass deviations of albumin matching peptides was tested in an attempt to improve the statistical significance of identifications and iterative searching was employed for identification of multiple proteins from each PMF.