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

Li X, Wu M, Kwoh CK, Ng SK - BMC Genomics (2010)

Bottom Line: Experimental 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Infocomm Research, 1 Fusionopolis Way, Singapore. xlli@i2r.a-star.edu.sg

ABSTRACT

Background: Most 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.

Results: Given 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.

Conclusions: Experimental 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.

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CODEC to detect protein complexes from TAP data which are modeled as a bipartite graph.
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Figure 2: CODEC to detect protein complexes from TAP data which are modeled as a bipartite graph.

Mentions: Recently, Geva et al. [66] proposed a new approach called CODEC to detect protein complexes from TAP data. Unlike above methods that convert TAP data to PPI networks, CODEC models the TAP data as a bipartite graph G = (U, V, E ) where U and V represent the sets of baits and preys respectively and E describes the bait-prey relationships detected in the experiments as shown in Figure 2. CODEC defines a likelihood ratio score for a candidate bipartite subgraph to measure its density versus the chance that it is randomly generated. CODEC first identifies candidate complexes from the neighborhood of each prey protein and then modifies them by adding or deleting vertices to maximize their likelihood ratio scores. Subsequently, CODEC filters the redundant candidates and obtains the final list of protein complexes. In Figure 2, {1, 2, 3, 4} is the set of baits and {5, 2, 6, 7, 8, 9} is the set of preys in this sample bipartite graph. CODEC finally predicts three protein complexes from this graph, namely {2, 6, 7, 8}, {2, 3, 7, 8} and {3, 4, 9} with likelihood ratio scores 2.39, 2.50 and 1.79, respectively.


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

Li X, Wu M, Kwoh CK, Ng SK - BMC Genomics (2010)

CODEC to detect protein complexes from TAP data which are modeled as a bipartite graph.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2822531&req=5

Figure 2: CODEC to detect protein complexes from TAP data which are modeled as a bipartite graph.
Mentions: Recently, Geva et al. [66] proposed a new approach called CODEC to detect protein complexes from TAP data. Unlike above methods that convert TAP data to PPI networks, CODEC models the TAP data as a bipartite graph G = (U, V, E ) where U and V represent the sets of baits and preys respectively and E describes the bait-prey relationships detected in the experiments as shown in Figure 2. CODEC defines a likelihood ratio score for a candidate bipartite subgraph to measure its density versus the chance that it is randomly generated. CODEC first identifies candidate complexes from the neighborhood of each prey protein and then modifies them by adding or deleting vertices to maximize their likelihood ratio scores. Subsequently, CODEC filters the redundant candidates and obtains the final list of protein complexes. In Figure 2, {1, 2, 3, 4} is the set of baits and {5, 2, 6, 7, 8, 9} is the set of preys in this sample bipartite graph. CODEC finally predicts three protein complexes from this graph, namely {2, 6, 7, 8}, {2, 3, 7, 8} and {3, 4, 9} with likelihood ratio scores 2.39, 2.50 and 1.79, respectively.

Bottom Line: Experimental 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Infocomm Research, 1 Fusionopolis Way, Singapore. xlli@i2r.a-star.edu.sg

ABSTRACT

Background: Most 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.

Results: Given 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.

Conclusions: Experimental 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.

Show MeSH