Zhang Xiao-Fei, Dai Dao-Qing, Ou-Yang Le, Yan Hong
Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang Road West, 510275 Guangzhou, China.
BMC Bioinformatics. 2014 Jun 13;15:186. doi: 10.1186/1471-2105-15-186.
Identification of protein complexes can help us get a better understanding of cellular mechanism. With the increasing availability of large-scale protein-protein interaction (PPI) data, numerous computational approaches have been proposed to detect complexes from the PPI networks. However, most of the current approaches do not consider overlaps among complexes or functional annotation information of individual proteins. Therefore, they might not be able to reflect the biological reality faithfully or make full use of the available domain-specific knowledge.
In this paper, we develop a Generative Model with Functional and Topological Properties (GMFTP) to describe the generative processes of the PPI network and the functional profile. The model provides a working mechanism for capturing the interaction structures and the functional patterns of proteins. By combining the functional and topological properties, we formulate the problem of identifying protein complexes as that of detecting a group of proteins which frequently interact with each other in the PPI network and have similar annotation patterns in the functional profile. Using the idea of link communities, our method naturally deals with overlaps among complexes. The benefits brought by the functional properties are demonstrated by real data analysis. The results evaluated using four criteria with respect to two gold standards show that GMFTP has a competitive performance over the state-of-the-art approaches. The effectiveness of detecting overlapping complexes is also demonstrated by analyzing the topological and functional features of multi- and mono-group proteins.
Based on the results obtained in this study, GMFTP presents to be a powerful approach for the identification of overlapping protein complexes using both the PPI network and the functional profile. The software can be downloaded from http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/GMFTP.zip.
蛋白质复合物的识别有助于我们更好地理解细胞机制。随着大规模蛋白质 - 蛋白质相互作用（PPI）数据的日益可得，人们提出了许多计算方法来从PPI网络中检测复合物。然而，当前大多数方法并未考虑复合物之间的重叠或单个蛋白质的功能注释信息。因此，它们可能无法如实地反映生物学现实，也无法充分利用可用的领域特定知识。
在本文中，我们开发了一种具有功能和拓扑特性的生成模型（GMFTP）来描述PPI网络和功能概况的生成过程。该模型提供了一种捕捉蛋白质相互作用结构和功能模式的工作机制。通过结合功能和拓扑特性，我们将识别蛋白质复合物的问题表述为在PPI网络中检测一组频繁相互作用且在功能概况中具有相似注释模式的蛋白质。利用链接社区的思想，我们的方法自然地处理了复合物之间的重叠。实际数据分析证明了功能特性带来的益处。使用针对两个金标准的四个标准进行评估的结果表明，GMFTP相对于现有最先进的方法具有竞争力。通过分析多组和单组蛋白质的拓扑和功能特征，也证明了检测重叠复合物的有效性。
基于本研究获得的结果，GMFTP是一种利用PPI网络和功能概况识别重叠蛋白质复合物的强大方法。该软件可从http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/GMFTP.zip下载。
