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Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

机译:使用依赖于参数的域-域相互作用重建蛋白质相互作用网络

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Background We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge. Results We present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches. Conclusions We have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions. Executable files and examples are given at: http://www.bhsai.org/downloads/padds/ webcite
机译:背景技术我们可以将蛋白质-蛋白质相互作用(PPI)描述为介导蛋白质之间物理相互作用的一组不同的域-域相互作用(DDI)。实验数据证实DDI比其相应的PPI更一致,这支持DDI分析可能会增进我们对PPI的理解并进一步了解细胞功能,疾病和进化的观点。但是,当前可用的实验DDI数据仅覆盖所有现有PPI的一小部分,并且在没有结构数据的情况下,确定哪个特定DDI介导任何给定的PPI是一个挑战。结果我们提出了对领域相互作用分析领域的两个贡献。首先,我们引入一种新颖的计算策略来合并来自多个数据库的域注释数据。我们表明,当我们合并来自六个注释数据库的酵母域注释时,我们将每个蛋白质的平均域数从1.05增加到2.44,使其更接近于估计的平均值3。其次,我们引入了一种新的计算方法,该方法依赖于参数给定一组PPI的DDI选择(PADDS)会提取一小部分结构域对,这些结构对可以重建蛋白质相互作用的原始集合,同时尝试最大程度地减少假阳性。基于来自多种生物的一组PPI,我们的方法比现有的计算方法提取了27%的实验检测DDI。结论我们提供了一种合并来自多个来源的域注释数据的方法,可确保任何给定生物的大型且一致的域注释。此外,我们提供了一种从基础PPI集中提取少量DDI的方法,并且表明与现有方法相比,我们的方法不偏向出现次数低或高的DDI。最后,我们使用这两种方法来突出显示基础注释密度对提取的DDI的特征的影响。尽管增加的注释极大地扩展了可能的DDI,但对真正的生物假阳性相互作用的认识的缺乏仍然阻止了负责所有蛋白质网络相互作用的域相互作用的明确分配。可执行文件和示例在以下位置提供:http://www.bhsai.org/downloads/padds/ webcite

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