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Functional coherence in domain interaction networks.

机译:域交互网络中的功能一致性。

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MOTIVATION: Extracting functional information from protein-protein interactions (PPI) poses significant challenges arising from the noisy, incomplete, generic and static nature of data obtained from high-throughput screening. Typical proteins are composed of multiple domains, often regarded as their primary functional and structural units. Motivated by these considerations, domain-domain interactions (DDI) for network-based analyses have received significant recent attention. This article performs a formal comparative investigation of the relationship between functional coherence and topological proximity in PPI and DDI networks. Our investigation provides the necessary basis for continued and focused investigation of DDIs as abstractions for functional characterization and modularization of networks. RESULTS: We investigate the problem of assessing the functional coherence of two biomolecules (or segments thereof) in a formal framework. We establish essential attributes of admissible measures of functional coherence, and demonstrate that existing, well-accepted measures are ill-suited to comparative analyses involving different entities (i.e. domains versus proteins). We propose a statistically motivated functional similarity measure that takes into account functional specificity as well as the distribution of functional attributes across entity groups to assess functional similarity in a statistically meaningful and biologically interpretable manner. Results on diverse data, including high-throughput and computationally predicted PPIs, as well as structural and computationally inferred DDIs for different organisms show that: (i) the relationship between functional similarity and network proximity is captured in a much more (biologically) intuitive manner by our measure, compared to existing measures and (ii) network proximity and functional similarity are significantly more correlated in DDI networks than in PPI networks, and that structurally determined DDIs provide better functional relevance as compared to computationally inferred DDIs.
机译:动机:从蛋白质-蛋白质相互作用(PPI)中提取功能信息提出了巨大的挑战,这是由于高通量筛选获得的数据的嘈杂,不完整,通用和静态性质所致。典型的蛋白质由多个结构域组成,通常被视为其主要功能和结构单元。基于这些考虑,基于网络的分析的域域交互(DDI)受到了近期的广泛关注。本文对PPI和DDI网络中功能一致性和拓扑邻近度之间的关系进行了正式的比较研究。我们的研究为继续和集中研究DDI作为网络功能描述和模块化的抽象提供了必要的基础。结果:我们调查了在正式框架中评估两个生物分子(或其片段)的功能一致性的问题。我们建立了功能一致性的可接受度量的必要属性,并证明了现有的,公认的度量不适用于涉及不同实体(即域与蛋白质)的比较分析。我们提出了一种统计上有动机的功能相似性度量,该度量考虑了功能特异性以及实体组之间功能属性的分布,以统计学上有意义的和生物学上可解释的方式评估功能相似性。不同数据的结果(包括高通量和计算预测的PPI以及不同生物的结构和计算推断的DDI)显示:(i)以(生物学上)直观的方式捕获功能相似性和网络接近性之​​间的关系通过我们的措施,与现有措施相比,(ii)DDI网络中的网络邻近性和功能相似性比PPI网络中的相关性明显更高,并且结构确定的DDI与计算得出的DDI相比提供了更好的功能相关性。

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