Inferring domain-domain interactions from protein-protein interactions
Inferring domain-domain interactions from protein-protein interactions
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DOI：
10.1145/565196.565211
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发表时间：
2002-04
期刊：
影响因子：
7
通讯作者：
Minghua Deng;Shipra Mehta;Fengzhu Sun;Ting Chen
中科院分区：
文献类型：
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作者：
Minghua Deng;Shipra Mehta;Fengzhu Sun;Ting Chen
蛋白质-蛋白质相互作用是细胞和细胞内生化过程中的重要事件。几位研究人员已经承担了通过酵母双杂交试验来分析覆盖有机体所有基因的蛋白质-蛋白质相互作用的任务。蛋白质-蛋白质相互作用包括蛋白质结构域之间的物理相互作用。因此，在结构域水平上了解蛋白质相互作用可以提供蛋白质相互作用网络的全局视角，并可能扩展蛋白质的功能。在这项研究中，我们提出了一种最大似然法，从Uetz等人，2000和Ito等人，2001年的高通量双杂交实验中获得的5719个酵母蛋白质-蛋白质相互作用中推断结构域-结构域相互作用。我们预测的准确性是在蛋白质水平上衡量的。我们的研究包括以下三个结果：(1)使用推断的结构域-结构域相互作用预测蛋白质之间的相互作用，获得39.0%的特异度和79.7%的灵敏度；(2)我们预测的蛋白质-蛋白质相互作用与通过非双杂交系统的方法获得的MIPS(http://mips.gfs.de)蛋白质-蛋白质相互作用)有显著重叠；(3)我们预测的相互作用对的基因表达谱的平均相关系数显著高于Uetz和Ito的实验数据中的随机对和相互作用对。我们的方法在分析不完全数据集和处理各种实验误差方面表现出了稳健性。我们发现了一些新的蛋白质-蛋白质相互作用，如RPS0A与APG17的相互作用，TAF40与SPT3的相互作用，这与蛋白质的功能是一致的。
Protein-protein interactions are important events in cellular and biochemical processes within a cell. Several researchers have undertaken the task of analyzing protein-protein interactions covering all genes of an organism by using yeast two-hybrid assays. Protein-protein interactions involve physical interactions between protein domains. Therefore, understanding protein interactions at the domain level gives a global view of the protein interaction network, and possibly extends functions of proteins. In this study, we present a Maximum Likelihood approach to infer domain-domain interactions from the 5719 yeast protein-protein interactions obtained in the high throughput two-hybrid experiments by Uetz et al., 2000 and Ito et al., 2001. The accuracies of our predictions are measured at the protein level. Our study includes the following three results: (1) using the inferred domain-domain interactions, we predict interactions between proteins and achieve 39.0% specificity and 79.7% sensitivity; (2) our predicted protein-protein interactions have a significant overlap with the MIPS(http://mips.gfs.de) protein-protein interactions obtained by methods other than the two-hybrid systems; and (3) the mean correlation coefficient of the gene expression profiles for our predicted interacting pairs is significantly higher than that for random pairs as well as that of interacting pairs in Uetz's and Ito's experimental data. Our method has shown robustness in analyzing incomplete data sets and dealing with various experimental errors. We find several novel protein-protein interactions such as RPS0A interacting with APG17 and TAF40 interacting with SPT3, which are consistent with the functions of the proteins.