Protein Complex Prediction Based on Mutually Exclusive Interactions in Protein Interaction Network

Jung, Sung‐Hoon; Jang, Woo–Hyuk; Hur, Hee-Yung; Hyun, Bo-Ra; Han, Dongsoo

doi:10.1142/9781848163324_0007

Cited by 9 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jung et al [ 75 ] recently proposed a method to extract Simultaneous Protein Interaction Clusters (SPIC) by considering this issue. They used both MCODE [ 27 ] and LCMA [ 37 ] to generate initial clusters.…”

Section: Recent Emerging Techniquesmentioning

confidence: 99%

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

et al. 2010

View full text Add to dashboard Cite

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.

show abstract

Section: Recent Emerging Techniquesmentioning

confidence: 99%

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

et al. 2010

View full text Add to dashboard Cite

show abstract

“…To lessen the effect of them, one can add a pre-processing [ 88 ] for evaluating the reliability of the interactions, filtering the false positives, or predicting the false negatives, to improve the robustness of the clustering algorithms. Other than the adoption of pre-processing, several authors have suggested to develop robust clustering algorithms by integrating data from multiple sources, such as genomic data [ 89 - 91 ], structure information [ 92 ], gene expression [ 19 , 93 - 101 ], Gene Ontology (GO) annotations [ 48 , 102 , 103 ], etc. The approaches differ in the way the sources are combined.…”

Section: Combination With Other Information and Ensemblementioning

confidence: 99%

“…Jung et al [ 93 ] presented a method to detect protein complexes based on the integration of protein-protein interaction data and mutually exclusive interaction information which were drawn from structural interface data of protein domains. PSIMAP [ 105 ], a tool and Database for constructing interactomes, provides interfacial residue pairs in physical domain-domain interactions.…”

Section: Combination With Other Information and Ensemblementioning

confidence: 99%

Recent advances in clustering methods for protein interaction networks

et al. 2010

View full text Add to dashboard Cite

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed.

show abstract

“…Some other methods detect dense subgraphs as protein complexes by conducting local neighborhood search [ 12 - 15 ]. Additional information are also more and more exploited to improve the predictions, for example, functional information used in some above studies [ 11 , 14 ] and data of protein binding interfaces used in [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A core-attachment based method to detect protein complexes in PPI networks

et al. 2009

View full text Add to dashboard Cite

Background: How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.

show abstract

Protein Complex Prediction Based on Mutually Exclusive Interactions in Protein Interaction Network

Cited by 9 publications

References 0 publications

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

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

Recent advances in clustering methods for protein interaction networks

A core-attachment based method to detect protein complexes in PPI networks

Contact Info

Product

Resources

About