Computational Identification of Potential Molecular Interactions in Arabidopsis
Computational Identification of Potential Molecular Interactions in Arabidopsis
复制标题
拟南芥中潜在分子相互作用的计算鉴定
DOI：
10.1104/pp.109.141317
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发表时间：
2009-09-01
期刊：
影响因子：
7.4
通讯作者：
Chen, Xin
中科院分区：
文献类型：
--
作者：
Lin, Mingzhi;Hu, Bin;Chen, Xin
蛋白质相互作用网络的知识有助于分子机制的研究。已经建立了几个主要的存储库来收集和组织已报道的蛋白质相互作用。许多相互作用已经在几种模式生物中被报道，然而迄今为止，在这些主要数据库中可以发现的植物相互作用数量非常有限。因此，计算识别潜在的植物相互作用是促进相关研究的必要条件。在这项工作中，我们基于各种间接证据构建了一个支持向量机模型来预测拟南芥（Arabidopsis thaliana）潜在的蛋白质相互作用。在100次迭代的bootstrap评估中，我们预测的交互的置信度估计为48.67%，这些交互预计覆盖整个交互组的29.02%。我们的模型的敏感性通过一个独立的评估数据集进行验证，该数据集由新报告的相互作用组成，与模型训练和测试中使用的示例没有重叠。结果表明，我们的模型成功识别了28.91%的新相互作用，与预期的灵敏度（29.02%）相似。将该模型应用于所有可能的拟南芥蛋白对，得到了224,206个潜在的相互作用，这是目前预测的拟南芥相互作用最大、最准确的集合。为了便于使用我们的结果，我们提出了预测拟南芥相互作用资源，有详细的注释和更具体的相互作用置信度测量。该数据库和相关文件可在http://www.cls.zju.edu.cn/pair/免费获取。
Knowledge of the protein interaction network is useful to assist molecular mechanism studies. Several major repositories have been established to collect and organize reported protein interactions. Many interactions have been reported in several model organisms, yet a very limited number of plant interactions can thus far be found in these major databases. Computational identification of potential plant interactions, therefore, is desired to facilitate relevant research. In this work, we constructed a support vector machine model to predict potential Arabidopsis (Arabidopsis thaliana) protein interactions based on a variety of indirect evidence. In a 100-iteration bootstrap evaluation, the confidence of our predicted interactions was estimated to be 48.67%, and these interactions were expected to cover 29.02% of the entire interactome. The sensitivity of our model was validated with an independent evaluation data set consisting of newly reported interactions that did not overlap with the examples used in model training and testing. Results showed that our model successfully recognized 28.91% of the new interactions, similar to its expected sensitivity (29.02%). Applying this model to all possible Arabidopsis protein pairs resulted in 224,206 potential interactions, which is the largest and most accurate set of predicted Arabidopsis interactions at present. In order to facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource, with detailed annotations and more specific per interaction confidence measurements. This database and related documents are freely accessible at http://www.cls.zju.edu.cn/pair/.