Insights into protein-protein interfaces using a Bayesian network prediction method
Insights into protein-protein interfaces using a Bayesian network prediction method
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DOI：
10.1016/j.jmb.2006.07.028
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发表时间：
2006-09-15
影响因子：
5.6
通讯作者：
Westhead, David R.
中科院分区：
文献类型：
--
作者：
Bradford, James R.;Needham, Chris J.;Westhead, David R.
识别两个相互作用的蛋白质之间的界面为了解蛋白质的功能提供了重要线索，并与药物发现越来越相关。这里，表面斑块分析与贝叶斯网络相结合，在180个蛋白质的基准数据集上预测蛋白质-蛋白质结合位点的成功率为82%，比以前的工作提高了6%，远高于随机方法实现的36%。即使在缺少进化信息的情况下，也能达到类似的成功率，这是对我们之前无法自动处理不完整数据的方法的进一步改进。在对Mog1p家族的案例研究中，我们表明我们的贝叶斯网络方法可以帮助预测以前未表征的结合位点，并为蛋白质功能提供重要线索。在Mog1p本身上，检测到了参与SLN1-SKN7信号转导通路的一个可能的结合位点，以及一个RAN结合位点，以前只进行保守研究，尽管我们的自动化方法不使用同源蛋白。在该家族的其余成员(两个结构基因组学目标，和一个参与高等植物光系统11复合体的蛋白质)上，我们发现了新的结合位点，与Mog1p上的结合位点几乎没有对应关系。这些结果表明，Mog1p家族的成员结合到不同的蛋白质上，尽管有相同的整体折叠，但可能具有不同的功能。我们还通过成功地定位了乳头瘤病毒感染的蛋白质-蛋白质相互作用网络中涉及的一些结合位点，证明了我们的方法在药物发现工作中的适用性。在另一项单独的研究中，我们试图使用第二个贝叶斯网络在我们的数据集中区分两种类型的结合位点，专有和非专有。这被证明是困难的，尽管根据斑块大小、静电势和守恒性实现了一些分离。这就是两种相互作用的补丁类型之间的相似性，我们能够使用专有结合部位属性来预测非专有结合部位的位置，反之亦然。(C)2006爱思唯尔有限公司。保留所有权利。
Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6% on previous work and well above the 36% that would be achieved by a random method. A comparable success rate was achieved even when evolutionary information was missing, a further improvement on our previous method which was unable to handle incomplete data automatically. In a case study of the Mog1p family, we showed that our Bayesian network method can aid the prediction of previously uncharacterised binding sites and provide important clues to protein function. On Mog1p itself a putative binding site involved in the SLN1-SKN7 signal transduction pathway was detected, as was a Ran binding site, previously characterized solely by conservation studies, even though our automated method operated without using homologous proteins. On the remaining members of the family (two structural genomics targets, and a protein involved in the photosystem 11 complex in higher plants) we identified novel binding sites with little correspondence to those on Mog1p. These results suggest that members of the Mog1p family bind to different proteins and probably have different functions despite sharing the same overall fold. We also demonstrated the applicability of our method to drug discovery efforts by successfully locating a number of binding sites involved in the protein-protein interaction network of papilloma virus infection. In a separate study, we attempted to distinguish between the two types of binding site, obligate and non-obligate, within our dataset using a second Bayesian network. This proved difficult although some separation was achieved on the basis of patch size, electrostatic potential and conservation. Such was the similarity between the two interacting patch types, we were able to use obligate binding site properties to predict the location of non-obligate binding sites and vice versa. (c) 2006 Elsevier Ltd. All rights reserved.