COLANDER: A PROBABILITY-BASED SUPPORT VECTOR MACHINE ALGORITHM FOR AUTOMATIC SCR

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。 我们开发了一个基于概率的机器学习程序，漏斗，在数据库搜索之前识别极有可能代表磷酸肽的串联质谱图。基于离子陷阱CID-MS/MS实验，我们确定了具有统计学意义的磷肽串联质谱特征。从376个有效的磷肽谱和376个非磷肽谱计算了特征的统计。然后，一个基于概率的支持向量机(SVM)程序Cleander被训练成五个选定的特征。数据集来自LC/LC-MS/MS分析从蛋白分解的细胞、组织和合成的磷酸肽中大规模浓缩的磷酸肽。这些数据集被用来评估滤器选择含有PS/PT的磷肽串联质谱图的能力。当应用于未知串联质谱图时，漏斗可以常规地去除80%的串联质谱图，同时保留95%的磷肽串联质谱图。该程序将数据库搜索所花费的计算时间显著减少了60%-90%。此外，代表磷酸肽的预过滤串联质谱图可以在预定的假阳性率下增加磷酸肽的鉴定数量。

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 developed a probability-based machine-learning program, Colander, to identify tandem mass spectra that are highly likely to represent phosphopeptides prior to database search. We identified statistically significant diagnostic features of phosphopeptide tandem mass spectra based on ion trap CID MS/MS experiments. Statistics for the features are calculated from 376 validated phosphopeptide spectra and 376 nonphosphopeptide spectra. A probability-based support vector machine (SVM) program, Colander, was then trained on five selected features. Data sets were assembled both from LC/LC-MS/MS analyses of large-scale phosphopeptide enrichments from proteolyzed cells, tissues and synthetic phosphopeptides. These data sets were used to evaluate the capability of Colander to select pS/pT-containing phosphopeptide tandem mass spectra. When applied to unknown tandem mass spectra, Colander can routinely remove 80% of tandem mass spectra while retaining 95% of phosphopeptide tandem mass spectra. The program significantly reduced computational time spent on database search by 60-90%. Furthermore, prefiltering tandem mass spectra representing phosphopeptides can increase the number of phosphopeptide identifications under a predefined false positive rate.