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The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks.

Hsing M, Byler KG, Cherkasov A - BMC Syst Biol (2008)

Bottom Line: Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations.It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Graduate Studies, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada. mhsing@interchange.ubc.ca

ABSTRACT

Background: Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.

Results: A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.

Conclusion: The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

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Network coverage of different bait selection strategies in protein complex pull-down experiments, simulated in Saccharomyces cerevisiae.
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Figure 4: Network coverage of different bait selection strategies in protein complex pull-down experiments, simulated in Saccharomyces cerevisiae.

Mentions: The bait selection strategy, assisted by the hub classifier, was simulated in the experimental PIN of Saccharomyces cerevisiae. In the case of one-round selection, choosing baits that were predicted as hubs by the classifier has greatly increased the network coverage in comparison to random selection. For instance, as illustrated in Figure 4, when 15% of total proteins were selected as baits based on the result of the hub classifier, 42.39% of the network coverage was achieved. On the other hand, only 26.53% of the network coverage was generated by the random bait selection.


The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks.

Hsing M, Byler KG, Cherkasov A - BMC Syst Biol (2008)

Network coverage of different bait selection strategies in protein complex pull-down experiments, simulated in Saccharomyces cerevisiae.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553323&req=5

Figure 4: Network coverage of different bait selection strategies in protein complex pull-down experiments, simulated in Saccharomyces cerevisiae.
Mentions: The bait selection strategy, assisted by the hub classifier, was simulated in the experimental PIN of Saccharomyces cerevisiae. In the case of one-round selection, choosing baits that were predicted as hubs by the classifier has greatly increased the network coverage in comparison to random selection. For instance, as illustrated in Figure 4, when 15% of total proteins were selected as baits based on the result of the hub classifier, 42.39% of the network coverage was achieved. On the other hand, only 26.53% of the network coverage was generated by the random bait selection.

Bottom Line: Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations.It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Graduate Studies, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada. mhsing@interchange.ubc.ca

ABSTRACT

Background: Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.

Results: A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.

Conclusion: The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

Show MeSH
Related in: MedlinePlus