Methodology article

Modifying the DPClus algorithm for identifying protein complexes based on new topological structures

Min Li¹ , Jian-er Chen^1,2 , Jian-xin Wang¹ , Bin Hu¹ and Gang Chen¹

¹School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China

²Department of Computer Science, Texas A&M University, College Station, Texas 77843, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:398doi:10.1186/1471-2105-9-398

Published:	25 September 2008

Abstract

Background

Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.

Results

Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density. Following the approach of that of the previously proposed clustering algorithm DPClus which expands clusters starting from seeded vertices, we present a clustering algorithm IPCA based on the new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm IPCA recalls more known complexes than previously proposed clustering algorithms, including DPClus, CFinder, LCMA, MCODE, RNSC and STM.

Conclusion

The proposed algorithm based on the new topological structure makes it possible to identify dense subgraphs in protein interaction networks, many of which correspond to known protein complexes. The algorithm is robust to the known high rate of false positives and false negatives in data from high-throughout interaction techniques. The program is available at http://netlab.csu.edu.cn/bioinformatics/limin/IPCA webcite.