Xu Bin, Wang Yang, Wang Zewei, Zhou Jiaogen, Zhou Shuigeng, Guan Jihong
Department of Computer Science and Technology, Tongji University, 4800 Cao'an Road, Shanghai, 201804, China.
School of Software, Jiangxi Normal University, 99 Ziyang Avenue, Nanchang, 330022, China.
BMC Bioinformatics. 2017 Oct 16;18(Suppl 12):419. doi: 10.1186/s12859-017-1820-8.
Predicting protein complexes from protein-protein interaction (PPI) networks has been studied for decade. Various methods have been proposed to address some challenging issues of this problem, including overlapping clusters, high false positive/negative rates of PPI data and diverse complex structures. It is well known that most current methods can detect effectively only complexes of size ≥3, which account for only about half of the total existing complexes. Recently, a method was proposed specifically for finding small complexes (size = 2 and 3) from PPI networks. However, up to now there is no effective approach that can predict both small (size ≤ 3) and large (size >3) complexes from PPI networks.
In this paper, we propose a novel method, called CPredictor2.0, that can detect both small and large complexes under a unified framework. Concretely, we first group proteins of similar functions. Then, the Markov clustering algorithm is employed to discover clusters in each group. Finally, we merge all discovered clusters that overlap with each other to a certain degree, and the merged clusters as well as the remaining clusters constitute the set of detected complexes. Extensive experiments have shown that the new method can more effectively predict both small and large complexes, in comparison with the state-of-the-art methods.
The proposed method, CPredictor2.0, can be applied to accurately predict both small and large protein complexes.
从蛋白质-蛋白质相互作用（PPI）网络预测蛋白质复合物的研究已开展了十年。人们提出了各种方法来解决该问题的一些挑战性问题，包括重叠簇、PPI数据的高假阳性/阴性率以及多样的复合物结构。众所周知，当前大多数方法只能有效地检测大小≥3的复合物，而这些复合物仅占现有复合物总数的约一半。最近，有人提出了一种专门用于从PPI网络中寻找小复合物（大小 = 2和3）的方法。然而，到目前为止，尚无有效的方法能够从PPI网络中预测小（大小≤3）和大（大小>3）复合物。
在本文中，我们提出了一种名为CPredictor2.0的新方法，该方法可以在统一框架下检测小复合物和大复合物。具体而言，我们首先将功能相似的蛋白质分组。然后，采用马尔可夫聚类算法在每个组中发现簇。最后，我们将所有在一定程度上相互重叠的发现簇合并，合并后的簇以及其余簇构成检测到的复合物集合。大量实验表明，与现有最先进的方法相比，新方法能够更有效地预测小复合物和大复合物。
所提出的方法CPredictor2.0可用于准确预测小蛋白质复合物和大蛋白质复合物。
