Abstract
BackgroundProtein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph.ResultsWe propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy), containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC) in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision.ConclusionsMany real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.
Highlights
Protein complexes play an important role in cellular mechanisms
The main criterion used for protein complex prediction is cliques or dense subgraphs
The first data set was gathered by Aloy et al [32] and the other was released in the Munich Information Center for Protein Sequences (MIPS) [33] at the time of this work (September 2009)
Summary
Several methods have been presented to predict protein complexes in a protein interaction network. Interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. The main criterion used for protein complex prediction is cliques or dense subgraphs. Spirin and Mirny proposed the clique-finding and super-paramagnetic clustering with Monte Carlo optimization approach to find clusters of proteins [10]. Another method is Molecular Complex Detection (MCODE) [11], which starts with vertex weighting and finds dense regions according to given parameters. MCL partitions the graph by discriminating strong and weak flows in the graph
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