Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs
Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs
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DOI：
10.1038/nbt1315
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发表时间：
2007-06-01
影响因子：
46.9
通讯作者：
Fenyo, David
中科院分区：
文献类型：
--
作者：
Eriksson, Jan;Fenyo, David
真正全面的蛋白质组分析在系统生物学和生物标志物发现工作中是非常可取的。但是，实验设计的动态范围和检测灵敏度不足以满足非常宽的蛋白质丰度范围，阻碍了完整的蛋白质组学表征。综合分析工作的实验设计包括分离，然后是基于质谱的消化蛋白鉴定。由于结果通常被报道为鉴定的集合，没有关于遗漏的蛋白质组部分的信息，因此很难评估并可能产生误导。在这里，我们通过采取实验设计的整体视图和使用计算机模拟来估计任何给定实验的成功率来解决这个问题。我们的方法表明，对典型的实验设计进行简单的改变可以将蛋白质组分析的成功率提高5到10倍。
Truly comprehensive proteome analysis is highly desirable in systems biology and biomarker discovery efforts. But complete proteome characterization has been hindered by the dynamic range and detection sensitivity of experimental designs, which are not adequate to the very wide range of protein abundances. Experimental designs for comprehensive analytical efforts involve separation followed by mass spectrometry-based identification of digested proteins. Because results are generally reported as a collection of identifications with no information on the fraction of the proteome that was missed, they are difficult to evaluate and potentially misleading. Here we address this problem by taking a holistic view of the experimental design and using computer simulations to estimate the success rate for any given experiment. Our approach demonstrates that simple changes in typical experimental designs can enhance the success rate of proteome analysis by five- to tenfold.