Detecting protein complexes from noisy protein interaction data

Abstract

High-throughput experimental techniques have made available large datasets of experimentally detected protein-protein interactions. However, experimentally determined protein complexes datasets are not exhaustive nor reliable. A protein complex plays a key role in disease development. Therefore, the identification and characterization of protein complexes involved is crucial to the understanding of the molecular events under normal and abnormal physiological conditions. In this paper, we propose a novel graph mining algorithm to identify protein complexes. The algorithm first checks the quality of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of our proposed method, we present experimental results on yeast protein interaction data. The level of accuracy achieved is a strong argument in favor of the proposed method. Novel protein complexes were also predicted to assist biologists in their search for protein complexes. The datasets and programs are freely available from http://faculty.uaeu.ac.ae/nzaki/PE-WCC.htm.