WCOACH: Protein complex prediction in weighted PPI networks

Abstract

Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates. In this article, a semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks. Consequently, one of the well-known methods, COACH, has been improved to be compatible with weighted PPI networks for protein complex detection. The new method, WCOACH, is compared to the COACH, ClusterOne, IPCA, CORE, OH-PIN, HC-PIN and MCODE methods on several PPI networks such as DIP, Krogan, Gavin 2002 and MIPS. WCOACH can be applied as a fast and high-performance algorithm to predict protein complexes in weighted PPI networks. All data and programs are freely available at http://bioinformatics. aut.ac.ir/wcoach.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

“…To improve the accuracy of module detection, the integration of functional information is more and more used [101][102][103][104]. These methods exploit the GO terms which in some cases are used to compute a similarity score that measures the edge weight and drives the module detection [105,106].…”

Recruitment of histone modifications to assist mRNA dosage maintenance after degeneration of cytosine DNA methylation during animal evolution

“…Protein complex data for the fly were obtained from the Drosophila Protein Interaction Map (https://interfly .med.harvard.edu) (Guruharsha et al 2011). Protein complex data for the worm were predicted by WCOACH algorithm (http:// bioinformatics.aut.ac.ir/wcoach) (Kouhsar et al 2015) with default parameters (weighted, minimum size of clusters = 3, NA threshold = 0.85) based on the weighted protein interaction data compiled from seven molecular interaction databases (Huang et al 2016). For each ortholog set, its member genes were searched with Ensembl IDs against a list of genes encoding members of protein complexes.…”

Combining sequence and Gene Ontology for protein module detection in the Weighted Network

“…In recent years, a number of new approaches (Hwang et al, 2008;Inoue et al, 2010;Lecca and Re, 2015;Nepusz et al, 2012;Wu et al, 2009;Yu et al, 2015), utilizing some novel computational models to identify protein modules in a PPI network, has been emerging. Especially, the sources of other biological information have been recently employed to the detection of protein modules in PPI networks (Andreopoulos et al, 2009;Feng et al, 2010;Kouhsar et al, 2016;Lakizadeh et al, 2015;Li et al, 2015;Maraziotis et al, 2007). Though using computational approaches to detect protein functional modules in PPI networks has received considerable attention and researchers have proposed many detection ideas and schemes over the past few years, how to efficiently identify protein modules by means of multiple sources of biological information is still a vital and challenging scientific problem in computational biology.…”