Abstract
BackgroundProtein-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.ResultsA 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.ConclusionThe 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.
Highlights
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
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
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
Summary
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. The accumulated protein interaction data have further supported recent protein network analyses that demonstrated the scale-free organization of PINs, where the majority of proteins have a low number of interactions in the network, with a few highly-connected proteins ( called hubs) having a significant number of interacting partners [1,2] Such inhomogeneous network topology allows a PIN to be robust against random removal of protein nodes, but vulnerable to targeted removal of network hubs [15]. Previous studies have shown defined relationships between the degree of connectivity of proteins in PINs, their sequence conservation, and cellular essentiality properties [16,17] Those studies indicated that highly-connected proteins (or hubs) represent very attractive subjects for understanding cellular functions, identifying novel drug targets, and for use in the rational design of large-scale pull-down experiments
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