中华绒螯蟹是一种重要的经济水生动物。由于缺乏系统分析工具,其许多生物过程的调节机制仍然模糊。蛋白质-蛋白质相互作用网络(PIN)是系统分析调控机制的重要工具。在这项工作中,一种新颖的机器学习方法,DGO-SVM,应用于预测大肠杆菌的蛋白质-蛋白质相互作用(PPI),它的PIN被重建。有了域,生物过程,蛋白质的分子功能和亚细胞位置作为特征,DGO-SVM在家蚕中表现出优异的性能,人类和五种水生甲壳类动物,准确率为92-96%。使用DGO-SVM,重建了中华大肠杆菌的PIN,含有14,703种蛋白质和7,243,597种相互作用,其中35,604个相互作用与566个主要参与外源刺激反应的新蛋白相关,细胞大分子代谢和调节。DGO-SVM证明了生物过程,蛋白质的分子功能和亚细胞位置是精确预测PPI的重要因素。我们为中华大肠杆菌重建了最大的PIN,为调控机制分析提供了系统的工具。此外,PIN中与新蛋白相关的PPIs可能为分析中华大肠杆菌潜在特定生理过程的机制提供重要线索。
Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.