Zhang Xiao-Fei, Ou-Yang Le, Hu Xiaohua, Dai Dao-Qing
School of Mathematics and Statistics, Central China Normal University, Luoyu Road, Wuhan, 430079, China.
Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
BMC Genomics. 2015 Oct 5;16:745. doi: 10.1186/s12864-015-1944-z.
The identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells. With the development of affinity purification-mass spectrometry (AP-MS) techniques, several computational scoring methods have been proposed to detect protein interactions from AP-MS data. However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions. Consequently, less is known about the precise physical wiring diagram within cells.
In this paper, we develop a Binary Interaction Network Model (BINM) to computationally identify direct physical interactions from co-complex interactions which can be inferred from purification data using previous scoring methods. This model provides a mathematical framework for capturing topological relationships between direct physical interactions and observed co-complex interactions. It reassigns a confidence score to each observed interaction to indicate its propensity to be a direct physical interaction. Then observed interactions with high confidence scores are predicted as direct physical interactions. We run our model on two yeast co-complex interaction networks which are constructed by two different scoring methods on a same combined AP-MS data. The direct physical interactions identified by various methods are comprehensively benchmarked against different reference sets that provide both direct and indirect evidence for physical contacts. Experiment results show that our model has a competitive performance over the state-of-the-art methods.
According to the results obtained in this study, BINM is a powerful scoring method that can solely use network topology to predict direct physical interactions from AP-MS data. This study provides us an alternative approach to explore the information inherent in AP-MS data. The software can be downloaded from https://github.com/Zhangxf-ccnu/BINM.
蛋白质-蛋白质相互作用的识别对于理解细胞内的功能组织有很大帮助。随着亲和纯化-质谱（AP-MS）技术的发展，已经提出了几种计算评分方法来从AP-MS数据中检测蛋白质相互作用。然而，目前大多数方法都集中在共复合体相互作用的检测上，没有区分直接物理相互作用和间接相互作用。因此，对于细胞内精确的物理连接图了解较少。
在本文中，我们开发了一种二元相互作用网络模型（BINM），用于从共复合体相互作用中通过计算识别直接物理相互作用，而共复合体相互作用可以使用先前的评分方法从纯化数据中推断出来。该模型提供了一个数学框架，用于捕捉直接物理相互作用和观察到的共复合体相互作用之间的拓扑关系。它为每个观察到的相互作用重新分配一个置信度分数，以表明其作为直接物理相互作用的倾向。然后，将具有高置信度分数的观察到的相互作用预测为直接物理相互作用。我们在两个酵母共复合体相互作用网络上运行我们的模型，这两个网络是在相同的组合AP-MS数据上通过两种不同的评分方法构建的。针对提供物理接触的直接和间接证据的不同参考集，对通过各种方法识别的直接物理相互作用进行了全面的基准测试。实验结果表明，我们的模型比现有方法具有更强的竞争力。
根据本研究获得的结果，BINM是一种强大的评分方法，它可以仅使用网络拓扑结构从AP-MS数据中预测直接物理相互作用。本研究为我们提供了一种探索AP-MS数据中固有信息的替代方法。该软件可从https://github.com/Zhangxf-ccnu/BINM下载。
