Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach

R Ranjani Rani,D Ramyachitra,A Brindhadevi

doi:10.1038/s41598-019-47468-y

R Ranjani Rani, D Ramyachitra + Show 1 more

Open Access

https://doi.org/10.1038/s41598-019-47468-y

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Journal: Scientific Reports	Publication Date: Jul 31, 2019
Citations: 9	License type: open-access

Affiliation: Bharathiar University

Abstract

The accessibility of a huge amount of protein-protein interaction (PPI) data has allowed to do research on biological networks that reveal the structure of a protein complex, pathways and its cellular organization. A key demand in computational biology is to recognize the modular structure of such biological networks. The detection of protein complexes from the PPI network, is one of the most challenging and significant problems in the post-genomic era. In Bioinformatics, the frequently employed approach for clustering the networks is Markov Clustering (MCL). Many of the researches for protein complex detection were done on the static PPI network, which suffers from a few drawbacks. To resolve this problem, this paper proposes an approach to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach (DMCL-EHO). Initially, the proposed method divides the PPI network into a set of dynamic subnetworks under various time points by combining the gene expression data and secondly, it employs the clustering analysis on every subnetwork using the MCL along with Elephant Herd Optimization approach. The experimental analysis was employed on different PPI network datasets and the proposed method surpasses various existing approaches in terms of accuracy measures. This paper identifies the common protein complexes that are expressively enriched in gold-standard datasets and also the pathway annotations of the detected protein complexes using the KEGG database.

Highlights

High-throughput approaches have created a huge quantity of protein interactions that helps to discover the protein complexes from a large protein-protein interaction (PPI) network
The volume of PPI networks has been increased due to high-throughput experiments, the lack of accurate computational model for protein complex detection exists
Many of the existing researches were employed on the static PPI data that do not provide accurate biological results

Summary

Introduction

High-throughput approaches have created a huge quantity of protein interactions that helps to discover the protein complexes from a large PPI network. In recent times, various attempts on the clustering process of dynamic PPI network has been initiated along with the gene expression data to enhance the protein complex detection. The firefly optimization was employed along with Markov Clustering (F-MCL) on the dynamic PPI network for predicting complexes. In order to discard the drawbacks of the above-mentioned approaches, a novel approach was proposed to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach. One of the most important advantages for EHO is that it is the most computationally efficient and has less time consuming compared to F-MCL and other approaches This is because the unwanted noisy data (unclustered proteins) will be removed from the clan separating operation of EHO approach.

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