Geometric de-noising of protein-protein interaction networks.
Geometric de-noising of protein-protein interaction networks.
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DOI：
10.1371/journal.pcbi.1000454
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发表时间：
2009-08
影响因子：
4.3
通讯作者：
Przulj N
中科院分区：
文献类型：
--
作者：
Kuchaiev O;Rasajski M;Higham DJ;Przulj N
理解蛋白质-蛋白质相互作用（PPI）的复杂网络是后基因组时代的最大挑战之一。由于近年来实验生物技术的发展，包括酵母双杂交（Y2 H）、串联亲和纯化（TAP）和其他高通量蛋白质相互作用（PPI）检测方法，大量的PPI网络数据变得可用。然而，主要的问题是噪音和不完整的程度。例如，对于Y2 H筛查，人们认为假阳性率可能高达64%，假阴性率可能在43%至71%之间。TAP实验被认为具有可比的噪声水平。我们提出了一种新的技术来评估从实验研究中获得的PPI网络中的相互作用的置信水平。我们用它来预测新的相互作用，从而指导未来的生物实验。该技术是第一个利用目前PPI网络的最佳拟合网络模型，几何图。我们的方法实现了85%的特异性和90%的灵敏度。我们用它来分配置信度得分的物理蛋白质-蛋白质相互作用在人类PPI网络从BioGRID下载。使用我们的方法，我们预测了人类PPI网络中的251种相互作用，其中统计学上显著的一部分对应于共享共同GO术语的蛋白质对。此外，我们在HPRD数据库和更新版本的BioGRID中验证了我们预测的相互作用的统计学显著部分。实现该方法的数据和Matlab代码可从网站http://www.kuchaev.com/Denoising免费获得。蛋白质负责维持我们细胞功能的大部分生物“繁重”工作。然而，蛋白质通常不会单独工作;相反，它们通常结合在一起形成几何和化学复杂的结构，这些结构是为特定任务量身定制的。实验技术使我们能够检测两种类型的蛋白质是否能够结合在一起，或“相互作用”。这就创造了一个网络，如果两个蛋白质相互作用，那么它们就被连接在一起，就像我们可以把两个人在Facebook上联系起来一样。这种蛋白质-蛋白质相互作用网络已经为几种生物开发，使用一系列方法，所有这些方法都受到实验误差的影响。这些网络数据揭示了一种迷人而复杂的连接模式。特别是，已知蛋白质可以排列成低维空间，例如三维立方体，使得相互作用的蛋白质靠近在一起。我们的工作表明，这种结构可以用来分配置信水平记录蛋白质-蛋白质相互作用和预测新的相互作用，被忽略的实验。在测试中，我们预测了251种新的人类蛋白质-蛋白质相互作用，并通过文献整理独立验证了其中统计学上显著的数量。
Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise. We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising. Proteins are responsible for much of the biological ‘heavy lifting’ that keeps our cells functioning. However, proteins don't usually work alone; instead they typically bind together to form geometrically and chemically complex structures that are tailored for a specific task. Experimental techniques allow us to detect whether two types of proteins are capable of binding together, or ‘interacting’. This creates a network where two proteins are connected if they have been seen to interact, just as we could regard two people as being connected if they are linked on Facebook. Such protein-protein interaction networks have been developed for several organisms, using a range of methods, all of which are subject to experimental errors. These network data reveal a fascinating and intricate pattern of connections. In particular, it is known that proteins can be arranged into a low-dimensional space, such as a three-dimensional cube, so that interacting proteins are close together. Our work shows that this structure can be exploited to assign confidence levels to recorded protein-protein interactions and predict new interactions that were overlooked experimentally. In tests, we predicted 251 new human protein-protein interactions, and through literature curation we independently validated a statistically significant number of them.
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影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
12.3
作者：
Hart GT;Ramani AK;Marcotte EM
通讯作者：
Marcotte EM
DOI：
10.1155/wcn/2006/90652
发表时间：
2006-01-01
影响因子：
2.6
作者：
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通讯作者：
An, Fengguang
影响因子：
64.8
作者：
Gavin, AC;Aloy, P;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
5.8
作者：
Higham, Desmond J.;Rasajski, Marija;Przulji, Natasa
通讯作者：
Przulji, Natasa