DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

Ali Sabzinezhad,Saeed Jalili

doi:10.3389/fgene.2020.00567

Abstract

Detecting protein complexes from the Protein-Protein interaction network (PPI) is the essence of discovering the rules of the cellular world. There is a large amount of PPI data available, generated from high throughput experimental data. The enormous size of the data persuaded us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems, and so, the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and to reduce the noise of PPI. Gene expression data are also used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and to create a dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms. The source code and datasets of DPCT used can be found at https://github.com/alisn72/DPCT.

Highlights

Protein complexes are modules made up of some proteins, which become a group at a specific time and situation, to become a functional part of a biological process (Gavin et al, 2006)
We present a novel dynamic method to detect protein complexes from the TAP-Aware weighted protein-protein interaction (PPI) network (DPCT) which uses a memetic metaheuristic algorithm for biclustering gene expression data, which can detect more accurate biclusters and is time efficient rather than a genetic algorithm
To assess the quality of the proposed DPCT method, we use precision, recall and F-1 measures which are the common measurements for protein complex detection methods

Summary

Introduction

Protein complexes are modules made up of some proteins, which become a group at a specific time and situation, to become a functional part of a biological process (Gavin et al, 2006). Many methods have been proposed to detect protein complexes from PPI networks (Li et al, 2010). A basic method to detect protein complexes from the PPI network is clustering. MCL (Enright et al, 2002) proposes to detect protein complexes by clustering the PPI network using random walking. MCL is very useful and scalable but it cannot detect overlapping protein complexes. Adjacency matrices are created for both PPI and TAP data and ensemble learning is applied to detect protein complexes from each matrix. TINCD uses 11 state-of-the-art methods on the PPI network and five detection methods on TAP data to detect protein complexes.

Methods

Results

Discussion

Conclusion