From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks

From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks
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从功能到相互作用:基于蛋白质间相互作用网络准确预测蛋白质复合物的新范式

DOI:
10.1109/tcbb.2014.2306825
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发表时间:
2014-07
期刊:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
影响因子:
--
通讯作者:
Jihong Guan
Jihong Guan
中科院分区:
其他
文献类型:
--
作者:
Bin Xu;Jihong Guan

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蛋白质复合物的鉴定对于了解复合物的形成和蛋白质功能至关重要。高通量实验的最新进展提供了蛋白质-蛋白质相互作用(PPI)的大量数据集。基于复合体是 PPI 网络(简称 PIN)的密集子图的假设,已经提出了许多方法来使用图聚类方法来预测复合体。在本文中,我们介绍了一种用于蛋白质复合物检测的新型从功能到相互作用的范例。由于蛋白质通过形成复合物来发挥生物学功能,我们首先使用基因本体 (GO) 的生物过程 (BP) 注释对蛋白质进行聚类。然后,我们将得到的蛋白质簇映射到 PPI 网络(简称 PIN)上,从 PPI 网络中提取由聚类蛋白质组成的连通子图,并使用与子图中蛋白质有丰富链接的蛋白质节点扩展每个连通子图。这种扩展的子图被视为预测的复合体。我们将所提出的方法(称为 CPredictor)应用于酿酒酵母的两个 PPI 数据集,以预测蛋白质复合物。实验结果表明,CPredictor 优于现有方法。 CPredictor 出色的精度证明,从功能到相互作用的范式为蛋白质复合物的计算检测提供了一种新的有效方法。
Identification of protein complexes is critical to understand complex formation and protein functions. Recent advances in high-throughput experiments have provided large data sets of protein-protein interactions (PPIs). Many approaches, based on the assumption that complexes are dense subgraphs of PPI networks (PINs in short), have been proposed to predict complexes using graph clustering methods. In this paper, we introduce a novel from-function-to-interaction paradigm for protein complex detection. As proteins perform biological functions by forming complexes, we first cluster proteins using biology process (BP) annotations from gene ontology (GO). Then, we map the resulting protein clusters onto a PPI network (PIN in short), extract connected subgraphs consisting of clustered proteins from the PPI network and expand each connected subgraph with protein nodes that have rich links to the proteins in the subgraph. Such expanded subgraphs are taken as predicted complexes. We apply the proposed method (called CPredictor) to two PPI data sets of S. cerevisiae for predicting protein complexes. Experimental results show that CPredictor outperforms the existing methods. The outstanding precision of CPredictor proves that the from-function-to-interaction paradigm provides a new and effective way to computational detection of protein complexes.
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