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使用依赖参数的域-域相互作用重建蛋白质相互作用网络。

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

机构信息

Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.

出版信息

BMC Bioinformatics. 2013 May 7;14:154. doi: 10.1186/1471-2105-14-154.

Abstract

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/

摘要

背景

我们可以将蛋白质-蛋白质相互作用(PPIs)描述为一系列独特的结构域-结构域相互作用(DDIs),这些相互作用介导了蛋白质之间的物理相互作用。实验数据证实,DDIs 比它们对应的 PPIs 更具一致性,这支持了这样一种观点,即分析 DDI 可能有助于我们理解 PPI,并进一步深入了解细胞功能、疾病和进化。然而,目前可用的实验 DDI 数据仅涵盖了所有现有 PPI 的一小部分,而且在没有结构数据的情况下,确定哪些特定的 DDI 介导了任何给定的 PPI 是一个挑战。

结果

我们为结构域相互作用分析领域做出了两个贡献。首先,我们提出了一种从多个数据库合并结构域注释数据的新计算策略。我们发现,当我们合并来自六个注释数据库的酵母结构域注释时,我们将每个蛋白质的平均结构域数量从 1.05 增加到 2.44,更接近估计的 3 的平均值。其次,我们引入了一种新的计算方法,即依赖参数的 DDI 选择(PADDS),该方法给定一组 PPIs,可以从中提取一小部分结构域对,以重建原始的蛋白质相互作用集,同时尝试最小化假阳性。基于来自多个生物体的一组 PPIs,我们的方法提取了比现有计算方法多 27%的实验检测到的 DDI。

结论

我们提供了一种从多个来源合并结构域注释数据的方法,确保了任何给定生物体的大量一致的结构域注释。此外,我们提供了一种从潜在的 PPI 集中提取一小部分 DDI 的方法,并且我们表明,与现有方法相比,我们的方法不受 DDI 出现次数低或高的偏向。最后,我们使用这两种方法来强调基础注释密度对提取的 DDI 特征的影响。尽管增加的注释大大扩展了可能的 DDI,但对真正的生物假阳性相互作用的知识缺乏仍然阻止了对负责所有蛋白质网络相互作用的结构域相互作用的明确分配。可执行文件和示例可在以下网址获得:http://www.bhsai.org/downloads/padds/

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/3660195/eb8e2ac3c13c/1471-2105-14-154-1.jpg

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