Shen Xianjun, Yi Li, Jiang Xingpeng, He Tingting, Hu Xiaohua, Yang Jincai
School of Computer, Central China Normal University, Wuhan, China.
College of Computing and Informatics, Drexel University, Philadelphia, United States of America.
PLoS One. 2016 Apr 21;11(4):e0153967. doi: 10.1371/journal.pone.0153967. eCollection 2016.
The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.
识别时间性蛋白质复合物将极大地有助于我们了解蛋白质相互作用网络（PINs）中的动态组织特征。最近的研究集中于将基因表达数据整合到静态PIN中以构建动态PIN，从而揭示蛋白质相互作用的动态进化过程，但在实践中它们未能识别出高表达或低表达水平蛋白质的活跃时间点。我们用一种名为偏差度的新方法构建了一个时间演化PIN（TEPIN），该方法旨在基于蛋白质自身表达值的偏差度来识别其活跃时间点。此外，由于蛋白质相互作用之间存在差异，我们用连接亲和力和基因共表达对TEPIN进行加权，以量化这些相互作用的程度。为了验证我们方法的有效性，将ClusterONE、CAMSE和MCL算法应用于TEPIN、DPIN（一种用最先进的三西格玛方法构建的动态PIN）和SPIN（原始的静态PIN）来检测时间性蛋白质复合物。在匹配度、灵敏度、特异性、F值和功能富集等方面，我们的TEPIN上的每种算法都优于其他网络上的算法。总之，我们的偏差度方法成功消除了先前最先进的动态PIN构建方法中存在的缺点。此外，蛋白质相互作用的生物学性质在我们的加权网络中能够得到很好的描述。加权TEPIN是检测时间性蛋白质复合物和揭示细胞组织动态蛋白质组装过程的一种有用方法。
