PCD-DPPI: Protein complex detection from dynamic PPI using shuffled frog-leaping algorithm

Abstract

In the post-genome period, the identification of a protein complex from large PPI (protein-protein interaction) networks is a challenging task. Protein complexes plays a vital character in numerous molecular processes of the cell. Although, the maximum computational techniques of protein complex detection have aimed at static PPI networks that cannot represent the logical dynamicity of protein interactions. In recent times, the dynamicity of PPI networks has been exploited by building a set of dynamic PPI subnetworks with respect to every time-point in a gene expression matrix. This paper presents a new technique, called Protein Complex Detection based on Dynamic PPI (PCD-DPPI) to accomplish dynamicity in protein complex detection. The initial stage of the proposed technique is based on shuffled frog-leaping algorithm, an Optimization technique that take out a few sets of genes that are co-regulated beneath some conditions from the input gene expression matrix. An individual extracted gene set is defined as a bicluster. In the second stage, depending on the biclusters, few dynamic PPI subnetworks are taken out from the input static PPI network. By employing a complex detection approach on every dynamic PPI subnetworks, protein complexes are identified and the result is aggregated. The proposed and existing algorithms were applied to various datasets such as DIP, Krogan “Extended”, Krogan “Core”, Gavin2, Gavin6, PPI 1, Collins and Gavin + Krogan. Experimental results have been proved that the proposed PCD-DPPI efficiently depicts the dynamicity characteristics in static PPI networks and attains expressively better results when compared to other existing standard methods.