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

Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

Mitra Mirzarezaee1, Babak N Araabi23 and Mehdi Sadeghi45*

Author Affiliations

1 Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

2 Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

3 School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran

4 National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran

5 School of Computer Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran

For all author emails, please log on.

BMC Systems Biology 2010, 4:172  doi:10.1186/1752-0509-4-172

Published: 19 December 2010

Abstract

Background

It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.

Results

We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%.

Conclusions

We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species.