Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is predicted as a high-degree vertex of protein interaction network. However, interaction data are usually incomplete and an essential protein cannot have high-connection due to data deficiency. Then, it is critical to design informative networks from other biological data sources. In this paper, we defined a minimal set of proteins to disrupt the maximum number of protein complexes. We constructed a weighted graph using a set of given complexes. We proposed a more appropriate method based on betweenness values to diagnose a minimal set of proteins whose removal would generate the disruption of protein complexes. The effectiveness of the proposed method was benchmarked using given dataset of complexes. The results of our method were compared to the results of other methods in terms of the number of disrupted complexes. Also, results indicated significant superiority of the minimal set of proteins in the massive disruption of complexes. Finally, we investigated the performance of our method for yeast and human datasets and analyzed biological properties of the selected proteins. Our algorithm and some example are freely available from http://bs.ipm.ac.ir/softwares/DPC/DPC.zip.
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