Martin Shawn, Mao Zisu, Chan Linda S, Rasheed Suraiya
Department of Computational Biology, Sandia National Laboratories, Albuquerque, NM 87185-1316, USA.
Int J Bioinform Res Appl. 2007;3(4):480-92. doi: 10.1504/IJBRA.2007.015416.
Present day approaches for the determination of protein-protein interaction networks are usually based on two hybrid experimental measurements. Here we consider a computational method that uses another type of experimental data: instead of direct information about protein-protein interactions, we consider data in the form of protein complexes. We propose a method for using these complexes to provide predictions of protein-protein interactions. When applied to a dataset obtained from a cat melanoma cell line we find that we are able to predict when a protein pair belongs to a complex with approximately 96% accuracy. Further, we are able to extrapolate the experimentally identified interaction pairs to the entire cat proteome.
目前用于确定蛋白质-蛋白质相互作用网络的方法通常基于双杂交实验测量。在这里，我们考虑一种使用另一种类型实验数据的计算方法：我们不考虑关于蛋白质-蛋白质相互作用的直接信息，而是考虑蛋白质复合物形式的数据。我们提出了一种利用这些复合物来预测蛋白质-蛋白质相互作用的方法。当应用于从猫黑色素瘤细胞系获得的数据集时，我们发现我们能够以大约96%的准确率预测一对蛋白质是否属于一个复合物。此外，我们能够将实验确定的相互作用对推断到整个猫蛋白质组。
