Predicting overlapping protein complexes in weighted interactome networks

Abstract

Protein complexes play important roles in integrating individual gene products to perform useful cellular functions. The increasing mount of protein-protein interaction (PPI) data has enabled us to predict protein complexes. In spite of the advances in these computational approaches and experimental techniques, it is impossible to construct an absolutely reliable PPI network. Taking into account the reliability of interactions in the PPI network, we have constructed a weighted protein-protein interaction (WPPI) network, in which the reliability of each interaction is represented as a weight using the topology of the PPI network. As overlaps are likely to have biological importance, we proposed a novel method named WN-PC (weighted network-based method for predicting protein complexes) to predict overlapping protein complexes on the WPPI network. The proposed algorithm predicts neighborhood graphs with an aggregation coefficient over a threshold as candidate complexes, and binds attachment proteins to candidate complexes. Finally, we have filtered redundant complexes which overlap other complexes to a very high extent in comparison to their density and size. A comprehensive comparison between competitive algorithms and our WN-PC method has been made in terms of the F-measure, coverage rate, and P-value. We have applied WN-PC to two different yeast PPI data sets, one of which is a huge PPI network consisting of over 6000 proteins and 200 000 interactions. Experimental results show that WN-PC outperforms the state-of-the-art methods. We think that our research may be helpful for other applications in PPI networks.