Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments.
Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments.
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DOI：
10.1002/pmic.201100537
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发表时间：
2012-05
期刊：
影响因子：
3.4
通讯作者：
Nesvizhskii, Alexey I.
中科院分区：
文献类型：
--
作者：
Nesvizhskii, Alexey I.
关键词：
使用亲和纯化和质谱（AP/MS）分析蛋白质相互作用网络和蛋白质复合物是蛋白质组学技术最常用和最成功的应用之一。AP/MS数据的最大挑战之一是未过滤数据集中存在大量假阳性蛋白质相互作用。在这里，我们回顾了AP/MS实验中检测特定蛋白质相互作用伙伴的计算和信息学策略，重点是不完整的（与全基因组相反）相互作用组作图研究。这些策略包括标准的统计方法，针对特定类型数据优化的经验评分方案，以及高级计算框架。这些方法的共同点是使用无标记的定量信息，例如可以从AP/MS数据中提取的光谱计数或积分肽强度。我们还讨论了相关的问题，如结合多个生物或技术复制，并处理使用不同的标记策略生成的数据。计算方法的基准评分方法进行了讨论，并强调需要生成参考AP/MS数据集。最后，我们讨论了更扩展的实验AP/MS数据建模的可能性，包括与外部信息的整合，如基于功能基因组学数据的蛋白质相互作用预测。
Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false positive protein interactions present in unfiltered datasets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome-wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS datasets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.
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