Multifunctional proteins revealed by overlapping clustering in protein interaction network

Abstract

Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters.Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein–protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks.Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197Contact: brun@tagc.univ-mrs.frSupplementary information: Supplementary data are available at Bioinformatics online.

A global network-based protocol for functional inference of hypothetical proteins in Synechocystis sp. PCC 6803

“…The direct annotation scheme is guided by the principle that proteins that lie closer to one another in the network are more likely to have a similar function (Deng et al, 2003;Karaoz et al, 2004;Letovsky and Kasif, 2003;Mostafavi et al, 2008;Vazquez et al, 2003), while the module-assisted scheme defines protein modules based on connectivity of different proteins in the network and then assigns possible functions to proteins based on associated member proteins with known functions (Arnau et al, 2005;Becker et al, 2012). So far a number of algorithms have been developed for both direct-and moduleassisted function predictions, and a fraction of them have even been implemented and supported with graphical interfaces (Sharan et al, 2007).…”

Provably Fast Inference of Latent Features from Networks

“…Applications of inter- est include learning social circles [29,43], distributed computation [7], detecting multifunctional proteins [12], mining collaboration, word-association and protein interaction graphs [36], link prediction [32,37] and studying cancer [22] among many others.…”

Protein Function Prediction with Incomplete Annotations

“…Several of these approaches take advantage of kernel functions to capture the similarity between gene expression sequences and employ kernel-based classifiers to predict protein functions [4], [5]. Some methods use PPI and graph-(or network-) based classifiers to predict the functions of proteins [1], [6], [7], [8], [9]. Several approaches predict protein functions by using heterogeneous data sources (including amino acid sequences and PPI) [2], [10], [11], [12].…”