Bayesian proteoform modeling improves protein quantification of global proteomic measurements.
Bayesian proteoform modeling improves protein quantification of global proteomic measurements.
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贝叶斯蛋白质组模型改进了全局蛋白质组测量的蛋白质定量。
DOI：
10.1074/mcp.m113.030932
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Waters
中科院分区：
文献类型：
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作者：
Webb-Robertson,Bobbie-JoM;Matzke,MelissaM;Datta,Susmita;Payne,SamuelH;Kang,Jiyun;Bramer,LisaM;Nicora,CarrieD;Shukla,AnilK;Metz,ThomasO;Rodland,KarinD;Smith,RichardD;Tardiff,MarkF;McDermott,JasonE;Pounds,JoelG;Waters
随着基于质谱的蛋白质组学的能力已经成熟，可以同时测量数万个肽，这具有提供蛋白质表达的系统视图的好处。然而，一个主要的挑战是，随着吞吐量的增加，从天然测量的肽的蛋白质定量估计已成为一个计算任务。现有的计算驱动的蛋白质定量方法的局限性在于大多数忽略了蛋白质变异，例如RNA转录物的选择性剪接和翻译后修饰或其他可能的蛋白质形式，这将影响蛋白质组的显著部分。这种假设的结果是，蛋白质水平的统计推断，以及因此的下游分析，如网络和途径建模，对于生物标志物的发现只有有限的能力。在这里，我们描述了一个贝叶斯蛋白质定量模型（BP-Quant）1，该模型使用统计学推导的肽特征来识别主导模式之外的肽或存在多个过表达模式，以提高相对蛋白质丰度估计。它是一种研究驱动的方法，利用在标准统计假设的背景下定义的实验目标，来鉴定一组与蛋白质表现出相似统计行为的肽。这种方法推断，相对蛋白质丰度的变化可以用作功能变化的替代，而不必考虑差异翻译后修饰、加工或剪接在改变蛋白质功能中的作用。我们使用来自小鼠血浆样品的稀释研究验证了该方法，并证明BP-Quant在蛋白质型鉴定方面达到了与当前最先进方法相似的准确度，具有更好的特异性。BP-Quant可作为MatLab®和R软件包提供。
As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)1that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.