Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
IEEE Trans Nanobioscience. 2013 Sep;12(3):165-72. doi: 10.1109/TNB.2013.2264097. Epub 2013 Aug 21.
Various computational algorithms are developed to identify protein complexes based on only one of specific topological structures in protein-protein interaction (PPI) networks, such as cliques, dense subgraphs, core-attachment structures and starlike structures. However, protein complexes exhibit intricate connections in a PPI network. They cannot be fully detected by only single topological structure. In this paper, we propose an algorithm based on multiple topological structures to identify protein complexes from PPI networks. In the proposed algorithm, four single topological structure based algorithms are first employed to identify raw predictions with specific topological structures, respectively. Those raw predictions are trimmed according to their topological information or GO annotations. Similar results are carefully merged before generating final predictions. Numerical experiments are conducted on a yeast PPI network of DIP and a human PPI network of HPRD. The predicted results show that the multiple topological structure based algorithm can not only obtain a more number of predictions, but also generate results with high accuracy in terms of f-score, matching with known protein complexes and functional enrichments with GO.
各种计算算法被开发出来，用于仅基于蛋白质-蛋白质相互作用 (PPI) 网络中的特定拓扑结构之一来识别蛋白质复合物，例如团簇、稠密子图、核心附着结构和星形结构。然而，蛋白质复合物在 PPI 网络中表现出复杂的连接。仅通过单个拓扑结构无法完全检测到它们。在本文中，我们提出了一种基于多种拓扑结构的算法，用于从 PPI 网络中识别蛋白质复合物。在提出的算法中，首先使用四种基于单个拓扑结构的算法分别识别具有特定拓扑结构的原始预测。根据拓扑信息或 GO 注释对原始预测进行修剪。在生成最终预测之前，仔细合并相似的结果。在 DIP 的酵母 PPI 网络和 HPRD 的人类 PPI 网络上进行了数值实验。预测结果表明，基于多种拓扑结构的算法不仅可以获得更多的预测，而且在 f-score 方面可以生成具有高精度的结果，与已知蛋白质复合物匹配，并进行 GO 的功能富集。
