Computational approaches for detecting protein complexes from protein interaction networks: a survey

Abstract

BackgroundMost proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions.ResultsGiven the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field.ConclusionsExperimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available.

Robust Community Detection Methods with Resolution Parameter for Complex Detection in Protein Protein Interaction Networks

“…the review [18]). Previous related works on multi resolution algorithms for clustering PPI networks either apply an algorithm multiple times with different values of the resolution parameter in order to investigate how proteins cluster at different resolution scales, e.g.…”

“…This latter investigation is motivated by the fact that PPI data are still not fully reliable, with the potential inclusion of both false positive and false negative interactions (see e.g. the discussion in [18]). Specifically, we consider fast greedy local search optimization of a clustering objective function with two different objective functions incorporating a resolution parameter: modularity [11] and a function we introduced in a recent work, called w-log-v [27].…”

“…Detecting protein complexes, consisting of proteins sharing a common function, is important, for instance for predicting a biological function of uncharacterized proteins. To this aim protein-protein interaction (PPI) networks have been used as a convenient graph-based representation for the comparative analysis and detection of (putative) protein complexes [18]. A PPI network is a graph where nodes are proteins and edges represent interactions between proteins.…”

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

“…Beginning from classical methods by Spirin & Mirny [11] and Bader & Hogue [12] that work primarily by clustering the network of protein interactions (PPI network), computational methods have come a long way, and current methods integrate diverse information with PPI networks to predict complexes. These methods have been tested extensively on data from model organisms [13,14], and are currently being extended to identify and catalogue complexes from less extensively mapped organisms including Homo sapiens [15].…”

RocSampler: Regularizing overlapping protein complexes in protein-protein interaction networks

“…The prediction of protein complexes from PPIs (see, for example, survey papers [1][2][3]) is one of the most challenging inference problems from PPIs because protein complexes are essential entities in the cell. Proteins' functions are manifested in the form of a protein *Correspondence: om@imi.kyushu-u.ac.jp 1 Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, 819-0395 Fukuoka, Japan Full list of author information is available at the end of the article complex.…”