Cluster-based assessment of protein-protein interaction confidence
Cluster-based assessment of protein-protein interaction confidence
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DOI：
10.1186/1471-2105-13-262
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发表时间：
2012-10-10
影响因子：
3
通讯作者：
Stelzl, Ulrich
中科院分区：
文献类型：
--
作者：
Kamburov, Atanas;Grossmann, Arndt;Stelzl, Ulrich
背景：蛋白质-蛋白质相互作用网络是系统级理解细胞生物学的关键。然而，交互数据可能包含相当一部分误报。已经提出了几种方法来评估个体相互作用的信心。其中大多数需要整合额外的数据，如蛋白质表达和相互作用同源性信息。虽然这些额外的数据肯定是有用的，但并不总是可用的，并且可能会引入额外的偏见和模糊性。结果：我们提出了一种新的基于网络拓扑的相互作用置信度评估方法，称为CAPPIC（基于聚类的蛋白质-蛋白质相互作用置信度评估）。它利用网络固有的模块化架构来评估个体交互的可信度。我们的方法从本质上确定算法参数，不需要任何参数输入或参考集进行置信度评分。结论：基于使用不同技术推断的五个酵母和两个人类物理相互作用组图，我们表明CAPPIC可靠地评估相互作用置信度，其性能优于其他基于网络拓扑的方法。置信度评分与相互作用蛋白的定位和生物过程注释的一致性相关。此外，它证实了物理相互作用的实验证据。我们的方法并不局限于物理相互作用组图，因为我们以一个大型酵母遗传相互作用网络为例。CAPPIC的实现可从http://intscore.molgen.mpg.de获得。
Background: Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.Results: We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network's inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.Conclusions: On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.de.