School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
Biomed Res Int. 2019 Aug 21;2019:3726721. doi: 10.1155/2019/3726721. eCollection 2019.
Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.
蛋白质复合物的鉴定对于揭示生物过程的潜在机制非常重要。许多计算方法已经被开发出来，用于从静态蛋白质-蛋白质相互作用（PPI）网络中鉴定蛋白质复合物。最近，研究人员开始考虑蛋白质-蛋白质相互作用的动态性。在细胞系统中，动态 PPI 网络更接近现实。预计可以从动态 PPI 网络中更准确地鉴定出更多的蛋白质复合物。在本文中，我们使用基因表达基准水平之上的波动程度来构建动态时间 PPI 网络，而不是使用基因表达水平。进一步地，我们将动态时间 PPI 网络转化为动态时间区间蛋白质相互作用网络（TI-PINs），并提出了一种新的方法来从构建的 TI-PINs 中准确地识别更多的蛋白质复合物。由于在时间区间内保留了连续的相互作用，因此构建的 TI-PINs 包含了更多的动态信息，以更准确地识别更多的蛋白质复合物。我们提出的识别方法使用多源生物数据来判断联合共定位条件、联合共表达条件和扩展簇条件是否满足，以确保识别出的蛋白质复合物具有共定位、共表达和功能同质性的特征。在酵母数据集上的实验结果表明，使用构建的 TI-PINs 可以比五个现有的动态 PPI 网络更好地识别蛋白质复合物，并且我们提出的识别方法可以比其他四种方法更准确地找到更多的蛋白质复合物。
