Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

Open Access Highly Accessed Research article

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

Michael Hsing1*, Kendall Grant Byler2 and Artem Cherkasov2

Author Affiliations

1 Bioinformatics Graduate Program, Faculty of Graduate Studies, University of British Columbia. 100-570 West 7th Avenue. Vancouver, BC, V5T 4S6, Canada.

2 Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia. D 452 HP, VGH. 2733 Heather Street. Vancouver, BC, V5Z 3J5, Canada.

For all author emails, please log on.

BMC Systems Biology 2008, 2:80  doi:10.1186/1752-0509-2-80

Published: 16 September 2008



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) [1,2]. 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.


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.


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.