An effective approach to detecting both small and large complexes from protein-protein interaction networks

BMC Bioinformatics

Research

Jihong Guan¹  Bin Xu¹  Yang Wang²  Shuigeng Zhou³  Zewei Wang⁴  Jiaogen Zhou⁵ 

[1] Department of Computer Science and Technology, Tongji University, 4800 Cao’an Road, 201804, Shanghai, China;School of Software, Jiangxi Normal University, 99 Ziyang Avenue, 330022, Nanchang, China;Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, 200433, Shanghai, China;The Bioinformatics Lab at Changzhou NO. 7 People’s Hospital, Changzhou, 213011, Jiangsu, China;Shanghai Southwest Model Middle School, 67 Huicheng Vallige-1, Baise Road, 200237, Shanghai, China;The institute of subtropical Agriculture, China Academy of Sciences, 444 Yuandaer Road, Mapoling, 410125, Changsha, China;

关键词: Small protein complex;    Large protein complex;    Protein-protein interaction;    Protein complex prediction;   

DOI  :  10.1186/s12859-017-1820-8

来源: Springer

【 摘 要 】

BackgroundPredicting 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.ResultsIn 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.ConclusionsThe proposed method, CPredictor2.0, can be applied to accurately predict both small and large protein complexes.

【 授权许可】

CC BY   
© The Author(s) 2017