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蛋白质-蛋白质相互作用作为亚细胞定位的预测指标。

Protein-protein interaction as a predictor of subcellular location.

作者信息

Shin Chang Jin, Wong Simon, Davis Melissa J, Ragan Mark A

机构信息

The University of Queensland, Institute for Molecular Bioscience, and ARC Centre of Excellence in Bioinformatics, QLD, Australia.

出版信息

BMC Syst Biol. 2009 Feb 25;3:28. doi: 10.1186/1752-0509-3-28.

Abstract

BACKGROUND

Many biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.

RESULTS

Using publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence (empirical approach, multiplicity of databases, and multiplicity of literature citations) and three based on type of biological evidence (inferred biological process, domain-domain interactions, and orthology relationships), with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins.

CONCLUSION

Understanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions.

摘要

背景

许多生物学过程是由蛋白质之间以及蛋白质内部的动态相互作用介导的。为了发生相互作用,两种蛋白质必须在空间和时间上同时出现。由于蛋白质-蛋白质相互作用(PPI)和亚细胞定位(SCL)是通过不同的经验方法发现的,PPI和SCL注释是独立的,并且在帮助我们理解单个蛋白质在细胞网络中的作用方面可能会相互补充。我们期望可靠的PPI注释能够表明在体内相互作用的蛋白质共定位在同一细胞区室中。我们的目标是评估使用PPI注释来确定人、小鼠、果蝇和酵母中蛋白质的SCL的潜力,并识别和量化促成这种互补性的因素。

结果

使用公开可用的数据,我们评估了相互作用的蛋白质必须共定位在同一亚细胞区室中的假设。基于一个大型的、人工整理的PPI数据集,我们证明了相当一部分相互作用的蛋白质实际上是共定位的。我们开发了一种方法,在对相互作用本身有足够信心的情况下,根据其相互作用伙伴的SCL来预测蛋白质的SCL。通过使用六条支持证据可以减少假阳性PPI的频率,其中三条基于记录证据的类型(经验方法、数据库的多样性和文献引用的多样性),三条基于生物学证据的类型(推断的生物学过程、结构域-结构域相互作用和直系同源关系),生物学证据比记录证据更有效。在识别膜蛋白的SCL方面,我们的方法比四种现有的预测方法表现更好,对于可溶性蛋白,表现相当或更好。

结论

理解细胞系统需要了解相互作用蛋白质的SCL。我们展示了如何更有效地使用PPI数据来对可溶性蛋白和膜蛋白产生可靠的SCL预测。通过考虑分子相互作用的生物学背景,我们对细胞功能的理解还有进一步改进的空间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bcb/2663780/47b91e31b055/1752-0509-3-28-1.jpg

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