IMPROVING THE SUCCESS RATE OF PROTEOME ANALYSIS

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 在系统生物学和生物标记物发现工作中，真正全面的蛋白质组分析是非常可取的。但是，实验设计的动态范围和检测灵敏度阻碍了蛋白质组的完整表征，这不足以满足非常广泛的蛋白质丰度范围。综合分析工作的实验设计包括分离，然后基于质谱学鉴定消化的蛋白质。由于结果通常报告为鉴定的集合，没有关于遗漏的蛋白质组部分的信息，因此它们很难评估，并可能具有误导性。在这里，我们通过从整体上看待实验设计并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，在典型的实验设计中进行简单的更改可以将蛋白质组分析的成功率提高五到十倍。描述这项工作的手稿发表在《自然·生物技术》杂志上，最近的出版物如下： 建立基于质谱学的蛋白质分析模型。 首页--期刊主要分类--期刊细介绍--期刊细介绍 方法Mol Biol。2011；694：109-17。 蛋白质组学的模型化实验设计。 首页--期刊主要分类--期刊细介绍--期刊细介绍 方法Mol Biol。2010；673：223-30。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. 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. A manuscript describing this work was published in Nature Biotechnology and the more recent publications below: Modeling mass spectrometry-based protein analysis. Eriksson J, Feny¿ D. Methods Mol Biol. 2011;694:109-17. Modeling experimental design for proteomics. Eriksson J, Feny¿ D. Methods Mol Biol. 2010;673:223-30.