Milenković Tijana, Przulj Natasa
Department of Computer Science, University of California, Irvine, CA 92697-3435, USA.
Cancer Inform. 2008;6:257-73. Epub 2008 Apr 14.
Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.
We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein's local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.
Data is available upon request.
蛋白质是生命必需的大分子，因此了解其功能至关重要。即使对于像面包酵母这样简单且研究充分的生物体，功能未分类的蛋白质数量也很多。确定蛋白质功能的方法已从仅基于序列同源性靶向特定蛋白质，转向基于蛋白质 - 蛋白质相互作用（PPI）网络对整个蛋白质组进行分析。由于蛋白质相互作用以执行特定功能，分析PPI网络的结构特性可能为单个蛋白质的生物学功能、它们参与的蛋白质复合物甚至更大的亚细胞机器提供有用线索。
我们设计了一种敏感的图论方法来比较节点邻域的局部结构，结果表明在PPI网络中，节点的生物学功能与其局部网络结构密切相关。该方法将PPI网络中蛋白质的局部拓扑结构总结为称为蛋白质签名的图let度向量，并计算所有蛋白质对之间的签名相似性。我们在这种度量下将PPI网络中拓扑相似的蛋白质分组，并表明这些蛋白质组属于相同的蛋白质复合物，执行相同的生物学功能，定位于相同的亚细胞区室，并且具有相同的组织表达。此外，我们将我们的技术应用于蛋白质组规模的网络数据，并推断未分类蛋白质的生物学功能，这表明我们的方法可以为未来的实验研究（如疾病蛋白质预测）提供有价值的指导。
可根据要求提供数据。
