Sadygov Rovshan G
Department of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, TX, USA.
Electrophoresis. 2014 Dec;35(24):3498-503. doi: 10.1002/elps.201400053. Epub 2014 Jul 24.
We studied the use of peak deviations (PDs) for application in phosphoproteomics. Due to the differences in the mass defects, the PDs of samples containing mixtures of phosphorylated and nonphosphorylated peptides show bimodal distributions. The ratios of peak heights accurately predict the phosphoproteome content of a sample. In this work, we apply a signal-processing tool, singular value decomposition, to reveal characteristic features of the phosphorylated, nonphosphorylated, and mixed samples. We show that a simple application of singular value decomposition to the PD matrix (i) detects transitions from mostly phosphorylated samples to mostly nonphosphorylated samples, (ii) reveals modes of low-abundance species in the presence of the high-abundance species (e.g., phosphorylated peptides), and (iii) simplifies the interpretation of the clustering of a covariance matrix obtained from PDs. As the eigenfunctions of the inner-product of the data matrix (made from the PDs) are Hermite functions, we observe a change of sign in the transition from samples enriched in phosphorylated peptides to samples containing fewer phosphorylated peptides. The ordering of the singular values of the data matrix points in the direction of changes to the phosphorylation content. No peptide identifications from a database were used for this study.
我们研究了峰偏差（PDs）在磷酸化蛋白质组学中的应用。由于质量缺陷的差异，含有磷酸化和非磷酸化肽混合物的样品的PDs呈现双峰分布。峰高比能准确预测样品的磷酸化蛋白质组含量。在这项工作中，我们应用一种信号处理工具——奇异值分解，来揭示磷酸化、非磷酸化和混合样品的特征。我们表明，对PD矩阵简单应用奇异值分解可以（i）检测从主要是磷酸化样品到主要是非磷酸化样品的转变，（ii）在高丰度物种（如磷酸化肽）存在的情况下揭示低丰度物种的模式，以及（iii）简化对从PDs获得的协方差矩阵聚类的解释。由于数据矩阵（由PDs构成）内积的本征函数是埃尔米特函数，我们观察到从富含磷酸化肽的样品到含磷酸化肽较少的样品的转变中符号的变化。数据矩阵奇异值的排序指向磷酸化含量变化的方向。本研究未使用数据库中的肽鉴定结果。
